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KLDiscovery Inc. (“KLDiscovery” or the “Company”), a leading global provider of technology solutions for electronic discovery, information governance,

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The IT services giant says GenAI will transform ... how last month’s Ryder Cup tested out technology to improve how fans digitally consume the event, and find out how Zurich Insurance has ...

Information technology (IT) in Italy

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Be Informed

beinformed

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Hello Amazon aspirants!! I have recently cleared all interviews for the QAE II position at Amazon. I would like to share about the interview process, expectations, preparation tips and the resources I referred to during my preparation. Most of the information is already present on Web/YouTube. I am giving links to all resources here. Note: I am not giving any questions from my interview as I have signed disclosure not to share. About process Before jumping into preparation, first you need to understand the hiring process. After applying, I got a call from a recruiter. He explained me the process as below - Online Assessment — Link will be shared to us, and we will have around 1 week to finish that.
Phone Interview — This is 1 hour round, in which questions will be asked on 3 aspects: 1. Test cases ; 2. Coding; 3. Leadership Principles (Preparation is same as explained for Main interview)
Main Interview — 5 different rounds — 1 hr each (Total 5hrs). Here we have option to have all rounds on same day (there will be lunch break 1hr) OR we can divide them for 2 different days Below are those 5 rounds. Not necessarily in the same order. Online test: About testing process, behavioral and situation based questions. Objective type. 1. Coding 2. Test case Enumeration & Test data preparation 3. Test Planning / Strategy 4. Debugging round 5. Leadership Principles After completion of all the above interviews, the panel will meet and decide on your candidature. Within a week, the recruiter can provide you with feedback. Apart from this, go through the previous QA interview experiences from the Leetcode discussions section. *Important — One of the rounds in the process is “Leadership Principles”, where you will be asked certain situational based questions which you need to answer based on the examples in your experience. Similar leadership principles questions, 1–2 will be asked on other 4 rounds also. Around 30% of the other 4 rounds will be focussed on leadership principles questions and remaining 70% will be actual interviews about that round (Coding, Debugging, Testcases, TestPlanning). So, Leadership Principles questions are very important when you are preparing for an Amazon interview. Preparation: 1. Coding - First I started preparing for the Coding round. I got a Leetcode subscription. The difference with Leetcode subscription is, we will have more questions and we will have company tag names to the coding questions. I filtered all “Amazon” + “Easy” programs. In that, I have excluded questions related to Trees, graph related questions. Of the remaining challenges, I started solving them daily. Most of these challenges are based on Arrays, Strings, Collections. Initially, when I had no clue on the approach, I used to see the answer on the web and tried to understand(many YouTube videos are there for each question). In that way, I have improved and could solve new problems on my own. That way I did practice around 60 easy problems from Leetcode(There are around 200+ Amazon+Easy problems in Leetcode, related to Arrays, Strings, Collections). Apart from this, I have read past Amazon QAE interview experiences on Leetcode and different websites and collected some questions(around 15) and solved them too. In Total, I have solved around 75 problems. I repeated solving them 3–4 times. It doesn’t mean that we get interview questions from leetcode / previous questions. It only gives confidence to you to solve similar questions. I have focussed only on basic concepts — Arrays, Stings, Collections(HashMaps mostly) related programs. Also, refer this video - https://www.youtube.com/watch?v=cdeoqgEUSvE&embedable=true 2. Test case Enumeration & Test data preparation Here, you may be asked to list test cases for any given scenario. Do follow the video below for the tips on how to answer. These tips are very important. https://www.youtube.com/watch?v=Xz2oHPpz2hM&embedable=true Be prepared for 1–2 questions on leadership principles (mentioned on this topic at last) 3. Test Planning & Test Strategy Be prepared to write TestPlan / TestStrategy for any given scenario. You may be given any scenario, and will be asked to write / explain the test plan for it. Always, ask clarifying questions. https://www.guru99.com/how-to-create-test-strategy-document.html Be prepared for 1–2 questions on leadership principles (mentioned on this topic at last). 4. Debugging round In this round, you will be given an issue / bug and you will be asked to debug it. Basically, you need to explain the steps / checks you perform to isolate the issue. This video is helpful - https://www.youtube.com/watch?v=65_8WUtwDnE&embedable=true There are some sample questions here. Be prepared for 1–2 questions on leadership principles (mentioned on this topic at last). 5. Leadership Principles This is a very important area. Amazon has 14 leadership principles. You need to study and understand these principles. In the call with the recruiter, you will be informed if any of the 14 principles are not applicable to your role(QA). In my case, 2 are excluded. It means I need to prepare another 12 leadership principles. You will be given any situation based question and you need to answer it based on any related example in your past. Be prepared to explain it in detail with the background of the situation, what you did, how you did and results and learnings out of it. In many blogs and other experiences, it is recommended to have atleast 2 unique examples for each principle as the question may be repeated across the rounds and you should not repeat the same example/answer. Below is the great website, which helped me in understanding them, and know different types of questions based on each principle with sample answers. Take that as reference and build your own answers. https://interviewgenie.com/resources In the above page, go to this section - “The 16 Leadership Principles with Sample Questions and Answers That Get Results” That’s it for now! Please let me know if you have any doubts. Wish you all the best!! Hello Amazon aspirants!! I have recently cleared all interviews for the QAE II position at Amazon. I would like to share about the interview process, expectations, preparation tips and the resources I referred to during my preparation. Most of the information is already present on Web/YouTube. I am giving links to all resources here. Note: I am not giving any questions from my interview as I have signed disclosure not to share. About process About process Before jumping into preparation, first you need to understand the hiring process. After applying, I got a call from a recruiter. He explained me the process as below - Online Assessment — Link will be shared to us, and we will have around 1 week to finish that. Phone Interview — This is 1 hour round, in which questions will be asked on 3 aspects: 1. Test cases ; 2. Coding; 3. Leadership Principles (Preparation is same as explained for Main interview) Main Interview — 5 different rounds — 1 hr each (Total 5hrs). Here we have option to have all rounds on same day (there will be lunch break 1hr) OR we can divide them for 2 different days Online Assessment — Link will be shared to us, and we will have around 1 week to finish that. Phone Interview — This is 1 hour round, in which questions will be asked on 3 aspects: 1. Test cases ; 2. Coding; 3. Leadership Principles (Preparation is same as explained for Main interview) Main Interview — 5 different rounds — 1 hr each (Total 5hrs). Here we have option to have all rounds on same day (there will be lunch break 1hr) OR we can divide them for 2 different days Below are those 5 rounds. Not necessarily in the same order. Online test: Online test: About testing process, behavioral and situation based questions. Objective type. 1. Coding 1. Coding 2. Test case Enumeration & Test data preparation 2. Test case Enumeration & Test data preparation 3. Test Planning / Strategy 3. Test Planning / Strategy 4. Debugging round 4. Debugging round 5. Leadership Principles 5. Leadership Principles After completion of all the above interviews, the panel will meet and decide on your candidature. Within a week, the recruiter can provide you with feedback. Apart from this, go through the previous QA interview experiences from the Leetcode discussions section. *Important — One of the rounds in the process is “Leadership Principles”, where you will be asked certain situational based questions which you need to answer based on the examples in your experience. Similar leadership principles questions, 1–2 will be asked on other 4 rounds also. Around 30% of the other 4 rounds will be focussed on leadership principles questions and remaining 70% will be actual interviews about that round (Coding, Debugging, Testcases, TestPlanning). So, Leadership Principles questions are very important when you are preparing for an Amazon interview. So, Leadership Principles questions are very important when you are preparing for an Amazon interview. Preparation: Preparation: 1. Coding - 1. Coding - 1. Coding - First I started preparing for the Coding round. I got a Leetcode subscription. The difference with Leetcode subscription is, we will have more questions and we will have company tag names to the coding questions. I filtered all “Amazon” + “Easy” programs. In that, I have excluded questions related to Trees, graph related questions. Of the remaining challenges, I started solving them daily. Most of these challenges are based on Arrays, Strings, Collections. Initially, when I had no clue on the approach, I used to see the answer on the web and tried to understand(many YouTube videos are there for each question). In that way, I have improved and could solve new problems on my own. That way I did practice around 60 easy problems from Leetcode(There are around 200+ Amazon+Easy problems in Leetcode, related to Arrays, Strings, Collections). Apart from this, I have read past Amazon QAE interview experiences on Leetcode and different websites and collected some questions(around 15) and solved them too. In Total, I have solved around 75 problems. I repeated solving them 3–4 times. It doesn’t mean that we get interview questions from leetcode / previous questions. It only gives confidence to you to solve similar questions. I have focussed only on basic concepts — Arrays, Stings, Collections(HashMaps mostly) related programs. Also, refer this video - https://www.youtube.com/watch?v=cdeoqgEUSvE&embedable=true https://www.youtube.com/watch?v=cdeoqgEUSvE&embedable=true 2. Test case Enumeration & Test data preparation 2. Test case Enumeration & Test data preparation 2. Test case Enumeration & Test data preparation Here, you may be asked to list test cases for any given scenario. Do follow the video below for the tips on how to answer. These tips are very important. https://www.youtube.com/watch?v=Xz2oHPpz2hM&embedable=true https://www.youtube.com/watch?v=Xz2oHPpz2hM&embedable=true Be prepared for 1–2 questions on leadership principles (mentioned on this topic at last) 3. Test Planning & Test Strategy 3. Test Planning & Test Strategy 3. Test Planning & Test Strategy Be prepared to write TestPlan / TestStrategy for any given scenario. You may be given any scenario, and will be asked to write / explain the test plan for it. Always, ask clarifying questions. https://www.guru99.com/how-to-create-test-strategy-document.html https://www.guru99.com/how-to-create-test-strategy-document.html Be prepared for 1–2 questions on leadership principles (mentioned on this topic at last). 4. Debugging round 4. Debugging round 4. Debugging round In this round, you will be given an issue / bug and you will be asked to debug it. Basically, you need to explain the steps / checks you perform to isolate the issue. This video is helpful - https://www.youtube.com/watch?v=65_8WUtwDnE&embedable=true https://www.youtube.com/watch?v=65_8WUtwDnE&embedable=true There are some sample questions here . here Be prepared for 1–2 questions on leadership principles (mentioned on this topic at last). 5. Leadership Principles 5. Leadership Principles 5. Leadership Principles This is a very important area. Amazon has 14 leadership principles. 14 You need to study and understand these principles. In the call with the recruiter, you will be informed if any of the 14 principles are not applicable to your role(QA). In my case, 2 are excluded. It means I need to prepare another 12 leadership principles. You will be given any situation based question and you need to answer it based on any related example in your past. Be prepared to explain it in detail with the background of the situation, what you did, how you did and results and learnings out of it. In many blogs and other experiences, it is recommended to have atleast 2 unique examples for each principle as the question may be repeated across the rounds and you should not repeat the same example/answer. Below is the great website, which helped me in understanding them, and know different types of questions based on each principle with sample answers. Take that as reference and build your own answers. https://interviewgenie.com/resources https://interviewgenie.com/resources In the above page, go to this section - “The 16 Leadership Principles with Sample Questions and Answers That Get Results” “The 16 Leadership Principles with Sample Questions and Answers That Get Results” That’s it for now! Please let me know if you have any doubts. Wish you all the best!!

How I Cracked the Amazon QAE II Interview

Schools have to play a crucial role in safeguarding children from all kinds of threats, and one that often flies under the radar is cyber attacks. Unfortunately, schools are a gold mine for criminals with invaluable data related to personal information and financial details which can be sold to the highest bidder on the black market. This was the case towards the end of last year, as thousands of students’ data was leaked in the Louisiana school district. A supply chain attack is one method that has been gaining a lot of traction recently, whereby an attack on an outside provider can lead to the attacker being able to infiltrate the school’s digital infrastructure. If the outside provider has been granted access rights to use the network or other applications, then the attacker would only need to get through the third party’s defenses to infiltrate the school’s system. This type of attack is particularly interesting to criminals—and devastating to victims—because they can gain access to multiple districts and mountains of data through one vendor. Types of supply chain attacks include stealing security certificates, compromising software development tools, preinstalling malware on devices, and embedding malicious code in firmware. For example, if a hacker steals a certificate used to authenticate a software update, they can use it to distribute malware disguised as a legitimate update. In schools, where IT resources may already be stretched thin, detecting these kinds of sophisticated attacks is very difficult. Supply chain attacks are very broad and can impact organizations of all types and sizes. Let’s examine where they tend to come from and their impact before discussing how schools can better defend themselves. The Source of Supply Chain Attacks Supply chain attacks can come from a variety of places and, depending on the source, can bring their own set of risks. Commercial software is something that every school will have across its network, and hackers can insert malicious code into a common software application that would then gain access to any school using that particular software. The problem is that schools often depend on software updates to patch vulnerabilities, but the updates themselves are actually one of the most common areas to exploit. One of the most famous examples came a few years back in 2020, when the major software company, __SolarWinds, was hacked with a backdoor __inserted into one of their IT management tools, and thousands of government organizations and schools around the world were affected.Another common source comes from open-source software where schools use these solutions as they’re generally more scalable and cost-effective, but this can also leave them more vulnerable. During the development process, hackers can introduce vulnerabilities, and then they wreak havoc when the software is finally implemented. There was a clear incident of this earlier this year when a software engineer found a backdoor that had gone unnoticed after two years of a group calledJia Tan implementing versions of XZ Utils, providing hours of largely benign volunteer coding into the open-source software, Linux. Lastly, we need to touch on foreign-sourced threats, which can be dangerous when schools are using systems from outside the country in their networks. Some countries have varying controls on tech industries, so schools need to be careful. What is the Impact of Supply Chain Attacks on Schools? There can be a huge impact on schools that are victims of supply chain attacks, including the disruption of administrative functions and the educational process. One of the biggest risks obviously comes from data breaches, where student records, financial details, and even potentially health data can be leaked. This could easily lead to privacy violations and identity theft.Schools also depend on software for scheduling, grading, and virtual classrooms, and if that is compromised, it can cause huge interruptions, with students and teachers unable to access vital resources. Lastly, financial costs, although seemingly not as prevalent in a supply chain attack, can still be a consequence. Ransomware can lock critical systems through compromised software. Even if the ransom is not paid, restoring systems and implementing stronger security measures is expensive. What Are The Mitigation Strategies? There are various ways that schools can go about mitigating supply chain attacks, primarily schools must have cybersecurity measures in place to protect their systems. They are outlined by the Department for Education, and involve a range of steps, from simple steps such as adopting multi-factor authentication to more complex data protection impact assessments. Regular software updates are important, but they are far from being fool-proof, schools need to be informed about any potential vulnerabilities that could arise in the software that they use. This can be done by monitoring cyber news and having open lines of communication with their vendors to stay on top of emerging threats. Access control is another element that can’t go unnoticed. Schools need to always review who has access to their systems and ensure that permissions are limited to the necessary people. The final mitigation strategy to mention is testing out incident response plans. This should involve explaining what steps would need to be taken in the event of a supply chain attack and how to isolate compromised systems, restore data from backups, and communicate with stakeholders. These kinds of drills mean that staff are ready to act quickly and effectively when an attack occurs. Supply chain attacks are undoubtedly brutal, and hackers can exploit weaknesses in schools, and their impact can be devastating. However, following some of the steps outlined above can at least mitigate the potential impact if staff are dedicated to prioritizing cybersecurity and good hygiene. Schools have to play a crucial role in safeguarding children from all kinds of threats, and one that often flies under the radar is cyber attacks. Unfortunately, schools are a gold mine for criminals with invaluable data related to personal information and financial details which can be sold to the highest bidder on the black market. This was the case towards the end of last year, as thousands of students’ data was leaked in the Louisiana school district. Schools have to play a crucial role in safeguarding children from all kinds of threats, and one that often flies under the radar is cyber attacks. Unfortunately, schools are a gold mine for criminals with invaluable data related to personal information and financial details which can be sold to the highest bidder on the black market. This was the case towards the end of last year, as thousands of students’ data was leaked in the Louisiana school district . leaked in the Louisiana school district leaked in the Louisiana school district A supply chain attack is one method that has been gaining a lot of traction recently, whereby an attack on an outside provider can lead to the attacker being able to infiltrate the school’s digital infrastructure. If the outside provider has been granted access rights to use the network or other applications, then the attacker would only need to get through the third party’s defenses to infiltrate the school’s system. This type of attack is particularly interesting to criminals—and devastating to victims—because they can gain access to multiple districts and mountains of data through one vendor. Types of supply chain attacks include stealing security certificates, compromising software development tools, preinstalling malware on devices, and embedding malicious code in firmware. For example, if a hacker steals a certificate used to authenticate a software update, they can use it to distribute malware disguised as a legitimate update. In schools, where IT resources may already be stretched thin, detecting these kinds of sophisticated attacks is very difficult. Supply chain attacks are very broad and can impact organizations of all types and sizes. Let’s examine where they tend to come from and their impact before discussing how schools can better defend themselves. The Source of Supply Chain Attacks Supply chain attacks can come from a variety of places and, depending on the source, can bring their own set of risks. Commercial software is something that every school will have across its network, and hackers can insert malicious code into a common software application that would then gain access to any school using that particular software. The problem is that schools often depend on software updates to patch vulnerabilities, but the updates themselves are actually one of the most common areas to exploit. One of the most famous examples came a few years back in 2020, when the major software company, __ SolarWinds, was hacked with a backdoor __inserted into one of their IT management tools, and thousands of government organizations and schools around the world were affected. Another common source comes from open-source software where schools use these solutions as they’re generally more scalable and cost-effective, but this can also leave them more vulnerable. During the development process, hackers can introduce vulnerabilities, and then they wreak havoc when the software is finally implemented. There was a clear incident of this earlier this year when a software engineer found a backdoor that had gone unnoticed after two years of a group called Jia Tan implementing versions of XZ Utils , providing hours of largely benign volunteer coding into the open-source software, Linux. SolarWinds, was hacked with a backdoor Jia Tan implementing versions of XZ Utils Jia Tan implementing versions of XZ Utils Lastly, we need to touch on foreign-sourced threats, which can be dangerous when schools are using systems from outside the country in their networks. Some countries have varying controls on tech industries, so schools need to be careful. What is the Impact of Supply Chain Attacks on Schools? There can be a huge impact on schools that are victims of supply chain attacks, including the disruption of administrative functions and the educational process. One of the biggest risks obviously comes from data breaches, where student records, financial details, and even potentially health data can be leaked. This could easily lead to privacy violations and identity theft. Schools also depend on software for scheduling, grading, and virtual classrooms, and if that is compromised, it can cause huge interruptions, with students and teachers unable to access vital resources. Lastly, financial costs, although seemingly not as prevalent in a supply chain attack, can still be a consequence. Ransomware can lock critical systems through compromised software. Even if the ransom is not paid, restoring systems and implementing stronger security measures is expensive. What Are The Mitigation Strategies? There are various ways that schools can go about mitigating supply chain attacks, primarily schools must have cybersecurity measures in place to protect their systems. They are outlined by the Department for Education , and involve a range of steps, from simple steps such as adopting multi-factor authentication to more complex data protection impact assessments. outlined by the Department for Education outlined by the Department for Education Regular software updates are important, but they are far from being fool-proof, schools need to be informed about any potential vulnerabilities that could arise in the software that they use. This can be done by monitoring cyber news and having open lines of communication with their vendors to stay on top of emerging threats. Access control is another element that can’t go unnoticed. Schools need to always review who has access to their systems and ensure that permissions are limited to the necessary people. The final mitigation strategy to mention is testing out incident response plans. This should involve explaining what steps would need to be taken in the event of a supply chain attack and how to isolate compromised systems, restore data from backups, and communicate with stakeholders. These kinds of drills mean that staff are ready to act quickly and effectively when an attack occurs. Supply chain attacks are undoubtedly brutal, and hackers can exploit weaknesses in schools, and their impact can be devastating. However, following some of the steps outlined above can at least mitigate the potential impact if staff are dedicated to prioritizing cybersecurity and good hygiene.

How Can Schools Be Better Prepared for Supply Chain Attacks?

Your Complete Guide to Self-Hosting Umami Analytics with Vercel and Supabase

Rio: WebApps in Pure Python - Technical Description

This paper is available on arxiv under CC 4.0 license. Authors: (1) Pietro Ghezzi, Brighton & Sussex Medical School, Falmer, Brighton, UK; (2) Peter G Bannister, Brighton & Sussex Medical School, Falmer, Brighton, UK; (3) Gonzalo Casino, Communication Department, Pompeu Fabra University, Barcelona, Spain and Iberoamerican Cochrane Center, Barcelona, Spain; (4) Alessia Catalani, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (5) Michel Goldman, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (6) Jessica Morley, Oxford Internet Institute, University of Oxford, Oxford, UK; (7) Marie Neunez, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (8) Andreu Prados, Communication Department, Pompeu Fabra University, Barcelona, Spain, Iberoamerican Cochrane Center, Barcelona, Spain, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles, Oxford Internet Institute, University of Oxford, Oxford, UK, and Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain; (9) Mariarosaria Taddeo, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK; (10) Tania Vanzolini, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (11) Luciano Floridi, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK. Table of Links Abstract and Introduction Methods Web search and content analysis Results Discussion Conclusion and References Table 1. Example of classification of webpages Table 2. Percentage of vaccine-negative webpages in different SERPs Discussion The aim of this research was to investigate whether search engines focused on privacy preservation ranked webpages with higher and/or lower levels of IQ (interpreted as EBM based content i.e. vaccine-positive rather than vaccine-negative) differently to the primary commercial search engines. In turn, this answered the research question “What is the current relationship between search engines’ approach to privacy and the scientific quality of the information they return?”. The results indicate that currently privacy-enhancing search engines often give more visibility to webpages promoting vaccine hesitancy or with a clear anti-vaccine position than Google. This is in agreement with the findings from a recent study by the Economist which analyzed 175,000 news links returned by Google to demonstrate that the search engine’s algorithm favorstrustworthy publications 20. Reputation and trustworthiness are key factors included in Google’s ranking algorithm. In 2019, Google published search quality evaluation guidelines, 4 which define webpages containing information that may affect the users’ health or financial stability, as “your money your life” (YMYL) pages. These guidelines reveal that when rating YMYL webpages, Google looks at the three criteria of Expertise-Authority-Trustworthiness (EA-T) and states “High E-A-T information pages on scientific topics should be produced by people or organizations with appropriate scientific expertise and represent well-established scientific consensus on issues where such consensus exists”. Thus, in the case of Google, the bandwagon heuristic mechanism of assessing medical information credibility is working in favour of promoting IQ. This is perhaps because those responsible for driving up the ‘reputation’ of specific websites by, for example, linking to them, have higher levels of eHealth literacy and therefore act as pseudo-gatekeepers protecting those with lower eHealth literacy from poor IQ results by ensuring that websites providing inaccurate online health information have a low ranking in the search results. In the case of privacy-preserving engines this appears not to be working – potentially because they do not track ‘reputation’ factors, which could be seen as proxies for IQ and credibility, e.g. clicks and bounce-rate, and so have no gatekeepers (pseudo or otherwise) working to limit the circulation of misinformation. To our knowledge, the algorithms used by these ‘alternative’ search engines are not public - Qwant states that they use their own algorithms5 - but despite this, we found a large overlap between the SERP of Qwant and those of Bing and Ecosia. Likewise, Ecosia and Swisscows showed a similar overlap (70%) with Bing. This means that it is not possible to check what factors determine the outcome of the decision making processes of the search engines algorithms, and hence identify those elements that contribute to circulate misinformation. Even without being able to check the exact mechanisms for the overall poorer results, the results suggest that, currently, decisions made by search engines to prioritize privacypreservation may have a negative impact on the IQ of results returned to users in health contexts. The pledge to provide independent and unbiased results or not to promote or hide websites based on political or moral interests can be seen as ethically ambiguous, in view of the potential consequences of pointing to scientifically unsound health information. Medical ethics requires that patients give informed consent before treatment and must, therefore, be informed accurately of the risks and benefits associated with treatment, something that is not possible if the search engine provider used by an individual is returning results with low IQ. From this consequentialist perspective, it is inherently unethical choosing not to interfere with a search engine’s ranking algorithm to ensure ‘manually’ that results of higher IQ are prioritized, whilst those of lower IQ are suppressed. This builds on arguments already made in the wider literature about algorithm ethics 21,22. For example, the Association for Computing Machinery (ACM) code of ethics and professional conduct includes, as a general principle, “avoid harm” along with that of “respect privacy” 23, which aligns with the Hippocratic oath. Specifically, the implication that the current design of privacy-preserving search engine algorithms underestimates the need for evaluation of IQ fits into the wider discussion about algorithmic design and how to ensure design decisions are made to protect and incorporate key values such as IQ. It is necessary, but insufficient, to design search engine algorithms that index purely on ‘relevance’, they must also be designed to index on quality. The challenge lies in the ability to do this in a way that balances the need to accept different perspectives (particularly those that are rooted in different cultural, religious or social ideals) whilst also filtering for IQ. Supporters of such arguments, including the authors, note that this requirement necessitates making the workings of search engine algorithms more transparent 24 to ensure their ethical compliance. Providing information on vaccines that is based on misinformation or disinformation (including studies whose data or conclusions have been shown to be wrong) is a deceptive practice, which goes against the basic tenets of medical and business ethics 25. This is in line with those who argue that the promotion of ‘alternative medicine’ is unethical because it lacks evidence and transparency of clinical efficacy and should be considered ‘false advertising’ 26. Online service providers have a moral responsibilities to ensure that users access health information that is scientifically validate 27. Misinformation and disinformation concerning healthcare circulating on the internet can have severe consequences and lead to widespread harm. Consider the example of South African president Mbeki, who delayed introducing anti-retroviral drugs in favor of alternative medicine based on information obtained from HIV-denialist internet websites, a decision estimated to have resulted in over 300,000 deaths 28. More recently, a cancer patient died in China after following an alternative, non-approved therapy they found using a search engine 29. In response Chinese authorities issued new rules that require search engines to provide “objective, fair and authoritative results”. Moral responsibility follows on the level of harm that misinformation and disinformation on healthcare may cause. It could be argued that there is a greater onus on Google – and other commercial search engine providers – than on alternative search engines to take these considerations into account when designing or interfering with algorithms for the purposes of promoting ethical compliance, given their larger market share. Google currently has over 90% of the worldwide market share6 and therefore has the potential to indirectly ‘cause harm’, through the potential promotion of low IQ webpages on vaccinations, to a great many more people than any of the alternative providers. However, studies have shown that those who hold anti-authoritarian views, openness to (potentially) controversial opinions, and an interest in alternative medicine are, in some cases, already more likely to hold vaccine-hesitant beliefs 30. These are also the individuals who are most likely to use alternative search providers given their sensitivity to privacy and tracking concerns. Therefore, while the alternative providers might reach a proportionally smaller audience – they are reaching an audience that is already more receptive to antivaccine information 31, and therefore more vulnerable to its effects. In other words, unless the alternative providers take steps to rank vaccine-related search results according to IQ, they may cause greater harm to those they do reach, meaning that the net negative impact is still greater even though the number of individuals they reach is smaller.

Online Information of Vaccines: Discussion

At SXSW this year, , CEO of the Future Today Institute, once again delivered a session as a lighthouse in the "technology supercycle" reshaping our world. Webb, a renowned author and professor at NYU Stern School of Business, shared key insights from the in an enlightening one-hour session. Despite having to catch a rerun due to simultaneous sessions, here's what I gathered: Amy Webb 17th Annual Technology Trends Report The Technology Supercycle We're smack in the middle of a "technology supercycle era," driven by the convergence of three general-purpose technologies: Artificial Intelligence (AI) Biotechnology Connected Device/Sensor Ecosystem This supercycle has the potential to reshape our economy and society in ways unseen since the Industrial Revolution and the dawn of the internet age. AI Trends Despite the hype and popularity of AI, there are still unresolved and often misunderstood big issues, including bias, accountability, and the risk of open-source models being used for nefarious purposes. Generative AI is evolving rapidly, moving from text-based models to a "concept to concrete" paradigm that can embody broad ideas. Smart Device Ecosystem: A New Black Mirror Episode There's an explosion of interest and development in smart device and sensor technology. These tech products, which include "face computers" (VR/AR headsets) and wearables that can read human intention, feed more real-world data into AI through "large action models." This raises privacy and social scoring concerns. Biotechnology Breakthroughs Advances in synthetic biology and biological computers made from human brain cells (organoid intelligence) could move us beyond silicon-based computing. This could pave the way for faster, more efficient, and more powerful computing systems that require less energy. This aligns with the theory of exascale computers mentioned in the MIT Technology Trends Report. Societal Impacts and Recommendations The concentration of power among a group of tech leaders following a techno-utopian vision is concerning. Governments should establish new departments and organizations to plan for the disruptive effects of the technology supercycle on industries and jobs. Companies must start mapping out evolving value networks to anticipate disruptions and create future value. To Succeed Individually in the Technology Supercycle, Webb Suggests: Strive to be informed about trends shaping our era and their potential impacts on your business. Begin to adopt strategic foresight and long-term planning to anticipate and adapt to disruptive changes. Engage in discussions on shaping the technology supercycle towards a more equitable future for all, and take an active role. This session with Amy Webb offered a comprehensive roadmap for navigating the currents and undercurrents of the technology supercycle, emphasizing the importance of proactive engagement and strategic planning to thrive in the rapidly evolving digital landscape.

What Is the Technology Supercycle?

In today's crowded marketplace, attention is currency. Value-based marketing is the investment that earns it. In today’s highly competitive era where businesses fight it out each day for their share in the industry, consumers are bombarded with marketing messages from every corner. Newspapers are filled with rampant advertisements, social media feeds overflow with promotional content, email inboxes are inundated with marketing copies, and even physical spaces are no longer immune to the constant barrage of "buy this" or "try that." The marketing landscape has turned pretty tough, but what practices make businesses see themselves over the shore? Or, in this crowded and noisy marketplace, how can businesses stand out and truly connect with their target audience? The answer lies in value-based marketing— an approach that shifts the focus from simply promoting products or services and refraining from being advertorial to emphasizing on demonstrating the inherent value a brand can offer to its customers. It’s less about being bullish with your marketing campaigns and goals and more about staying focused on building genuine relationships, fostering trust, and ultimately, earning customer loyalty in a sustainable and meaningful way. Value-based marketing- How is it a winning strategy in a crowded marketplace Today's consumers are bombarded with messages, and since they do not like them, they've become quite adept at filtering out noise. Traditional and self-promotional marketing messages often get lost in the sea of information and leave the customers disenchanted with the business. Value-based marketing, however, cuts through the clutter by offering something different— insightful content, helpful solutions, and genuine connections. By focusing on value first, brands can grab the attention of their audience and establish themselves as a trusted resource, not just another advertisement. Cuts through the clutter- Living in a time when marketing appears boisterous and customers are growing increasingly skeptical about the claims businesses make, value-based marketing is something to watch out for. Modern-day consumers don't want to be sold to; instead, they want to be informed and empowered. Value-based marketing prioritizes delivering value to customers throughout the entire marketing process, rather than just focusing on making sales. This naturally facilitates trust through transparency, authenticity, and genuine concern for the customer's needs. By providing valuable information, educational content, and helpful resources, brands demonstrate their expertise and establish themselves as a trustworthy source. Fosters trust and credibility- The last few years in marketing have been largely characterized by emotions. Contemporary customers look for that emotional connection in whatever marketing is directed at them. Marketers too try to leverage the emotional element in their marketing programs through advertising, public relations, etc. For this reason and catering to modern-day needs, it goes beyond talking about features and benefits. Instead, it taps into the emotions, values, and aspirations of the target audience. By understanding their desires, fears, and motivations, businesses can craft marketing messages that resonate on a deeper level. This emotional connection is crucial for building brand loyalty and fostering long-term relationships with customers. Creates emotional connections- When you provide genuine value, it doesn't go unnoticed. When subjected to value-based marketing, consumers are more likely to share valuable content, recommend your brand, and engage with your marketing efforts if they perceive it as informative and helpful. This organic reach and engagement not only saves on marketing costs but also expands a brand's reach to a wider audience through word-of-mouth marketing and social sharing. It is this aspect of value-based marketing that generates tremendous organic reach and engagement for businesses. Generates organic reach and engagement- What, if not customer loyalty, can be more rewarding for a business? Value-based marketing goes beyond traditional marketing in that it doesn't stop at the point of sale but moves up to make the customers stay loyal. It does so by building long-term relationships with customers by consistently providing value even after the initial purchase. This can be achieved through excellent customer service, personalized experiences, and continued educational content that keeps your brand relevant and top-of-mind. By consistently demonstrating your commitment to your customers' well-being, you command great loyalty and encourage repeat business. Builds customer loyalty- Implementing Value-Based Marketing Even though value-based marketing can be significantly yielding for businesses, the question of “how to integrate value-based marketing into one’s business strategies” remains a tough nut to crack. Despite such decisional challenges that businesses routinely face, below are some ways through which businesses can integrate value-based practices in their activities. Understanding your ideal customer's needs, wants, and challenges is crucial for crafting relevant and valuable content. So, the identification of needs becomes preliminary. Identify your target audience- Don't just talk about your product or service— instead, explain how it solves real problems for your target audience. After all, no customer likes boasting and blabbering about one’s products because that’s the easiest thing to do, and gone are the days when customers used to fall for them. Focus on providing solutions- Create informative blog posts, helpful guides, insightful videos, or engaging social media content that educates and empowers your audience. Through this, businesses can establish a connection with them. Offer valuable content- Being phony can put a business in a precarious situation. Businesses should not make exaggerated claims or misleading statements. Instead, they can build trust by being honest and upfront about their brand and its offerings. Be authentic and transparent- It’s recommended that businesses be very keen to what their customers have to say. Engaging with the audience by actively listening to their voices & grievances, and promptly responding to their feedback and concerns, and using their insights to improve marketing efforts can help build relationships on a personal level. Listen and respond- Summing up Marketing has come of age. The modern day customers will fail to associate themselves with anything that is everything, but emotional. Businesses, therefore, ought to find novel ways of creating value for them and coming up with smart ways to let them know that they are held dearly and close to their hearts. To make this happen, value-based marketing emerges as a strong beacon of trust, authenticity, and genuine connection. By emphasizing value outreach to the target audience, contemporary businesses can cut through the clutter, build lasting relationships, and ultimately achieve sustainable business growth.

Why Value-Based Marketing Wins in a Crowded Marketplace

The recent revelation of the purchasing the sensitive internet data of Americans did not feel like a bombshell despite its potential to shake public trust in government surveillance practices. National Security Agency (NSA) No front-page headlines blared with outrage; no politicians clamored for accountability. It seemed as though the acceptance of ubiquitous surveillance had dulled our collective sense of shock. Let’s talk about this shocking revelation and its profound implications for our data security. The NSA's Purchasing of Browser Data: A Breach of Legal Protocol In the digital age, where our every click and keystroke can be tracked, privacy often feels like a distant memory. But what if the very guardians of our freedoms were covertly peering into our online lives without the necessary checks and balances? Recently, a revelation shook the foundations of trust in government surveillance. But the surprising aspect is there wasn't a bombshell or a breaking news alert. Rather, it was a quiet unveiling of documents that painted a stark picture of the NSA's covert activities. Senator Ron Wyden disclosed that the National Security Agency has been purchasing Americans' web browsing data from commercial data brokers without warrants. This means that government agencies regularly spy on Americans by acquiring confidential information, bypassing the court orders intended to safeguard individual privacy rights. What makes this revelation particularly damning is the hypocrisy it exposes. The United States has always been a symbol of freedom and democracy and was criticizing other states for surveillance practices. Yet, behind closed doors, it appears that the U.S. government was engaging in similar activities, albeit with a veneer of legality. But perhaps what's most troubling is the nonchalance with which this revelation was met. It underscores a dangerous complacency, a tacit acceptance of surveillance as the new normal. A Senator’s Call for Action Wyden fought for almost 3 years to publicly release the fact that the NSA is buying Americans’ internet records. He has even succeeded in obtaining public confirmation of that fact after  placing a hold on the nomination of Lt. General Timothy Haugh to serve as NSA director. Following Senator Wyden's previous efforts that compelled the FBI to admit its purchase of sensitive data, he is now urging all intelligence agencies to halt buying personal data acquired illegally by data brokers. Expressing concern, Wyden emphasized that the government should not support an industry that blatantly violates Americans' privacy, branding such actions as both unethical and illegal. He addressed a letter to Avril Haines, Director of National Intelligence, urging for the adoption of policies aligning with the legal standards set by the FTC for data sales. Questioning the Legality of Data Acquisition NSA officials briefed Senator Wyden, revealing that the agency not only purchases data on Americans within the US but also acquires their Internet metadata. Senator Wyden expressed concern about the implications of this revelation. He explained that accessing such data could expose personal and sensitive information, such as individuals' online activities, including visits to websites offering mental health support, resources for survivors of abuse, or providers of reproductive health services like birth control or abortion medication. The Role of Intelligence Agencies in Data Broker Violations Wyden highlighted the possibility that intelligence agencies might inadvertently aid data brokers in violating FTC regulations, which mandate clear disclosures and informed consent from users before their data is sold. Despite his seven-year investigation into data brokers, Wyden noted the absence of any company providing such warnings to users prior to data collection. Wyden referred to the case of X-Mode, a data broker recently scrutinized by the FTC for its questionable practices. This case appeared after the company admitted to selling sensitive location data without user consent, even after consent revocation. He described the FTC's order as establishing "new rules," although it primarily serves as a form of common law through settlements, indicating unacceptable practices under the FTC Act. X-Mode's violations of the FTC Act encompassed various infractions, including unfairly selling sensitive data, disregarding consumers' privacy choices, and deceptively collecting and using location data without consent verification. However, the FTC did not provide clarification on whether the order extends to data purchases by intelligence agencies. It's also important to mention that the FTC order includes exemptions for data collected outside the US for security or national security purposes by federal agencies or entities. Wyden Calls for Compliance with FTC Regulations Senator Wyden urged the Director of National Intelligence (DNI) to instruct US intelligence agencies to cease purchasing Americans' private data obtained unlawfully, as per the new rules outlined by the FTC quite recently. The FTC's recent ruling stipulated that Americans must be informed and give consent for their data to be sold to "government contractors for national security purposes." Also, to address this issue, Wyden called for intelligence agencies to conduct a thorough inventory and promptly erase any illegally obtained data on Americans without proper authorization. He argued that failing to do so allows agencies like the NSA and FBI to bypass constitutional protections like the Fourth Amendment effectively. Wyden's Recommendations to Ensure Compliance In addition to urging compliance with the FTC's regulations, Wyden proposed three actions for intelligence agency elements to ensure adherence to the latest rulings: Conduct Inventory of Purchased Personal Data Intelligence agencies should compile an inventory of the personal data purchased about Americans, encompassing aspects like location and internet metadata. This recommendation aligns with the suggestion made by the Office of the DNI's Senior Advisory Group Panel on Commercially Available Information in its January 2022 report. Assess Data Sources Against FTC Standards Each data source identified in the inventory should be evaluated to determine whether it meets the standards outlined by the FTC for legal personal data sales. This aligns with the Senior Advisory Group's recommendation to identify and safeguard sensitive Commercially Available Information that raises privacy and civil liberties concerns. Purge Non-Compliant Data If the data purchases do not meet the FTC's legal standards, intelligence agencies should promptly purge the data. Any specific need to retain such data should be communicated to Congress, and the details of retained data should be made known to the American public to the greatest extent possible. Wyden's call for intelligence agencies to inventory and purge unlawfully collected data signifies a crucial step toward accountability and rectification. By holding agencies accountable for their actions, we can mitigate the adverse impacts of unwarranted surveillance on individuals' rights. Responses to Waden on Shady Data Broker Dealings In reply to Wyden's letter, Ronald Moultrie, the Under Secretary of Defense for Intelligence & Security, assured that the Department of Defense (DoD) strictly follows rigorous standards for privacy and civil liberties when obtaining Americans' location data. He stated that there is no existing legal obligation for the DoD to acquire court orders for commercially available information accessible to foreign adversaries, US companies, and private individuals. In another response to Wyden, General Paul Nakasone, the leader of the NSA, said that the agency takes measures to limit the collection of data on US individuals and only acquires essential information relevant to its missions. This includes some commercially available data on Americans involved in international communications, crucial for safeguarding the US Defense Industrial Base and supporting military weapons systems. But Wyden is still not convinced. He’s still concerned about the widespread unethical practice of data brokers selling Americans' information without their consent, emphasizing the urgent need for regulation. No reason can be enough to justify the support of intelligence agencies for companies engaged in intrusive surveillance, as identified by the FTC. Though Moultrie indicated that the DNI determines which information sources are suitable for intelligence activities, Wyden is more concerned about secret data acquisition. Americans must be told and allowed to opt out of such secretive data collection. The lack of transparency from intelligence agencies surrounding data purchases is raising many red flags. Final Words It has been all over the internet for the last few years that the NSA has bought the browsing data of Americans. But quite recently, all these speculations have turned into reality as Senator Wyden has finally succeeded in getting the truth out of the NSA after 3 years of consistent efforts. Now, Wyden is demanding intelligence agencies remove all the sensitive data that should not be attained by anyone without a warrant. Additionally, Wyden pointed out that, according to the FTC's order, Americans should be informed and consent to their data being sold to government contractors for national security purposes. This requirement aims to safeguard individuals' privacy rights amidst concerns about data exploitation. While the FTC has taken action against a handful of data brokers, the questionable practice of selling data without obtaining Americans' consent is a widespread issue requiring regulation. Instead of being customers in this questionable market, intelligence agencies ought to cease funding companies accused of engaging in intrusive and unregulated surveillance of Americans.

NSA Finally Admits The Purchase Of Americans’ Sensitive Data

NSA অবশেষে আমেরিকানদের সংবেদনশীল ডেটা ক্রয় স্বীকার করে

Die NSA gibt schließlich den Kauf sensibler Daten von Amerikanern zu

La NSA finalmente admite la compra de datos confidenciales de estadounidenses

La NSA admet enfin l’achat de données sensibles par les Américains

एनएसए ने अंततः अमेरिकियों के संवेदनशील डेटा की खरीद को स्वीकार कर लिया

NSA、ついにアメリカ人の機密データの購入を認める

NSA, 마침내 미국인의 민감한 데이터 구매를 인정

NSA finalmente admite a compra de dados confidenciais dos americanos

АНБ наконец признало покупку конфиденциальных данных американцев

NSA Sonunda Amerikalıların Hassas Verilerinin Satın Alındığını Kabul Etti

NSA cuối cùng thừa nhận việc mua dữ liệu nhạy cảm của người Mỹ

美国国家安全局最终承认购买美国人的敏感数据

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jingjing Wang, School of Computing; (2) Joshua Luo, The Westminster Schools; (3) Grace Yang, South Windsor High School; (4) Allen Hong, D.W. Daniel High School; (5) Feng Luo, School of Computing. Table of Links Abstract & Introduction Related Work Methodology Experiment Results Discussion References Abstract This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method’s properties using unique network data from the universe of Facebook’s users and existing network data from a field experiment. : Experimental Design, Spillover Effects, Causal Inference, Cluster Designs. JEL Codes: C10, C14, C31, C54 Keywords 1 Introduction Consider a (large) population of n individuals connected under a single observed network. Researchers are interested in conducting an experiment to estimate the global average treatment effect, i.e., the difference between the average effect of treating all versus none of the individuals in the population. Treating an individual may generate spillovers to her friends in the network. To capture such effects, researchers conduct a cluster experiment. Individuals are first partitioned into clusters. Within a cluster, either all units are assigned to the treatment or all units are assigned to the control group. Finally, researchers estimate treatment effects by taking a difference between the average outcomes of treated and control units (possibly adjusting for baseline covariates). The cluster design does not require modeling the dependence of individual outcomes on neighbors’ assignments, but it requires a choice of clusters and some assumptions on the extend of the spillovers along the network. For example, cluster experiments on online platforms require choosing a partition of the social network, and field experiments require choosing the unit of randomization, such as villages or regions. This raises the question of how many and which clusters to use in experiments. Typical approaches in economic research assume prior knowledge of many independent clusters. There are many settings where this information is not available, and instead units in the population have different degrees of connections.[1] This paper provides an econometric framework to choose when and how to design the clusters in cluster experiments. Different from existing clustering algorithms geared towards community detection, we motivate the choice of the clusters based on the task of estimating global treatment effects. The choice of clustering must balance two competing objectives: the larger the clusters (and the smaller the number of clusters), the smaller the bias of the estimated global effect, but the larger its variance. We introduce an algorithmic procedure – entitled Causal Clustering – to choose the clustering that minimizes a weighted combination of the worst-case bias and variance as a function of the network and clusters. The worst-case approach encodes uncertainty over the dependence of individual outcomes on neighbors’ assignments. We study (i) whether to run a cluster-level instead of individual-level randomization; (ii) how to cluster individuals (and how many clusters to use). We focus on a class of models where spillover effects are small relative to the outcomes’ variance but possibly non-negligible for inference. This is formalized in a novel framework of local asymptotics where individual outcomes depend arbitrarily on neighbors’ treatments, and, as n grows, spillovers from neighbors (and possibly also direct effects) converge to zero, but at an arbitrary slow rate (e.g., slower than n −1/2 ). This framework encodes the researchers’ uncertainty on the presence (and magnitude) of spillover effects by modeling first-order neighbors’ effect local to zero, with the convergence rate capturing the expected magnitude of spillovers.[2] The local asymptotic framework we study is consistent with settings with small (but non-negligible) treatment and spillover effects, typical, for instance, in online experiments [e.g., Karrer et al., 2021]. We characterize optimal clustering as a function of the expected magnitude of the largest spillover effects that the experiment can generate. The largest size of the spillover effects is a key input in our algorithms, whose characterization, in practice, is necessary for the design of the experiment but can be challenging. This parameter can be informed by previous experiments, in the same spirit of minimum detectable effects used in power analysis [e.g. Baird et al., 2018], or using some particular modeling assumptions. We provide guidance to practitioners in Section 6. Our analysis proceeds as follows. First, we provide a formal characterization of the worstcase bias and variance. We show that the worst-case bias is closely related to a particular notion of between-clusters connectedness, defined as the per-individual average number of friends in other clusters. The worst-case variance can potentially be an arbitrary function of within-clusters covariances and between-clusters covariances: individuals in the same cluster have identical assignments and in different clusters may share common neighbors. We show that the variance only depends on the average squared cluster size, up to an asymptotically negligible error. This result formalizes the intuition that a larger number of clusters, with a small variance in cluster size, decreases the variance of the estimator. We draw the implications of these results for choosing between a cluster experiment (for a given clustering) or assigning treatments independently between individuals (i.e., Bernoulli design or completely randomized design). Suppose the magnitude of the spillover effects is smaller than the square root of the number of clusters. In that case, the variance component dominates the bias, and a Bernoulli design is preferred (where a Bernoulli design is a special case of a cluster design with clusters containing a single unit). Vice-versa, a cluster design is preferred if the bias dominates the variance. Intuitively, because our objective trades off the bias and variance of the estimator, whenever the number of clusters is small, it is best to run a Bernoulli design for any value of spillover effects local to zero. On the other hand, if the number of clusters is sufficiently large, and the cluster design appropriately controls the bias of the estimator, a cluster design is preferred. We provide practitioners with a simple decision rule between cluster and Bernoulli designs that only depends on the number of clusters and the expected spillover effects’ magnitude. We then turn to the design of the optimal clustering, where the choice is not whether to run a cluster or Bernoulli design, but rather which clustering to use. The choice of optimal clustering reduces to a novel penalized minimum cut optimization problem, with a penalty that depends on the variation in clusters’ size. The program admits a convenient formulation as a sequence of trace-optimization problems, each solved via off-the-shelf semidefinite programming algorithms. We provide an empirical application using unique network data from the universe of Facebook users. We show that our procedure provides an explicit ranking between different clustering algorithms implemented at scale at Facebook. We also illustrate trade-offs in the choice of the clustering algorithm and properties of the graph. A second application using network data from Cai et al. [2015] illustrates the method’s advantages for choosing clusters in field experiments. We show that the choice of the clusters based on village identity is sub-optimal in this application, because the number of village clusters is too small relative to the optimal clustering. We present an alternative choice of clusters. This paper connects to the literature on experimental design, causal inference with spillover effects, and clustering. Existing methods for experimental designs with spillover effects include cluster and saturation designs, studied in Baird et al. [2018], Basse and Feller [2016], Karrer et al. [2021], Pouget-Abadie [2018], Taylor and Eckles [2018], Viviano [2020b], among others. These papers provide an analysis of the properties of particular designs for a given clustering or clustering algorithm. Different from the current paper, these references either do not study the question of the optimal clustering algorithm for experimental design, or only provide heuristic comparisons of different clustering methods. A particular class of clustering algorithms is ε-net clustering algorithms – which consist of assigning sequentially individuals in the same neighborhood to the same cluster [Eckles et al., 2017, Ugander et al., 2013]. Variants of these algorithms have been recently studied in Leung [2022] for spatial spillovers and Faridani and Niehaus [2022] in more general nonEuclidean spaces. These papers provide an optimal rate of the clusters’ size as a function of how interference decays in space for approximately unbiased estimators. Here, instead, we provide an explicit trade-off and novel characterization of the bias and variance, leveraging local asymptotics. It allows us to characterize the optimal clustering as the solution of a trace-optimization program (different from ε-net clustering). The comparison of a cluster and Bernoulli designs through local asymptotics is also a novel contribution. Additional references of experiments with networks in the absence of cluster experiments are Basse and Airoldi [2018b], and Jagadeesan et al. [2020] for estimating direct instead of global average treatment effects studied here; Kang and Imbens [2016] who study encouragement designs, without focusing on the problem of mean-squared error optimal designs; Viviano [2020a] for experiments on networks using information from a pilot study; Basse and Airoldi [2018a] discuss limitations of design-based causal inference under interference. None of these paper study (optimal) cluster designs. The literature on treatment effects under network interference includes Aronow and Samii [2017], Hudgens and Halloran [2008], Manski [2013], Leung [2020], Athey et al. [2018], Goldsmith-Pinkham and Imbens [2013], S¨avje et al. [2021], Ogburn et al. [2017], Manresa [2013], Li and Wager [2022], Leung [2022], among others. The assumption that an individual depends on first-order degree connections is most closely related to Leung [2020].3 None of the above references study experimental designs. Finally, we relate more broadly to the literature on clustering in economics – see Wooldridge [2003], Leung [2023], Abadie et al. [2017] and references therein – with the difference that here we focus on optimal clustering, rather than taking the clusters as given – and the literature on graph clustering, including Von Luxburg [2007], Newman [2013a,b], Lei [2019], Lei et al. [2020], Li et al. [2022], and references therein. Different from this last set of papers on graph clustering, this paper focuses on treatment effect estimation, instead of community detection. The remainder of the paper is organized as follows: Section 2 presents the setup; Section
3 characterizes the bias and variance and contrast cluster and Bernoulli designs; Section
4 formalizes the optimization problem for the optimal clustering; Section 5 presents two
applications and numerical studies and Section 6 contains recommendations for practice. [1] For example, when using villages as clusters, individuals may interact in the same and nearby villages. See Egger et al. [2022] for an example in cash-transfer programs. [2] The assumption of spillovers within first-order neighbors can be relaxed here by assuming that higher order spillovers are an order of magnitude smaller than first-order spillovers.

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