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Obvious Technology

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When Neo's brain - portrayed by Keanu Reeves - is unplugged from the Matrix network in the 1999 cyberpunk movie , he observes that the human species has largely been turned into an energy source, powering a world that has been taken over by intelligent machines. Similarly humans today are waking up to the reality that technology companies have turned individuals into non-player characters, feeding corporate objectives. While law makers and government agencies have sprung into action to mitigate against the most apparent externalities of technologies optimized for profit, one fact has become apparent: The Matrix Regulation directed at corporations and humans can never catch up to technological innovation. Importantly, today's regulation efforts fail to address the core of the problem: the architecture of the internet itself. Current network topology - including maybe most visibly information distribution on the world wide web - positions human beings as of , requiring individuals to commit/loose their own upon engagement or entrance, while largely overriding the individual's objectives with those of network operators or other network users (often charitably referred to as "advertisers"). This architecture has relegated human activity to data-generating oracle functions. In the context of networks oracles devices are entities that connect a deterministic system with non-native digital information. consumers endpoints agency Current internet topology regards humans in much the same way as weather stations, and other IOT devices. Product You Entering a single letter into the search box of your 'search engine' (not really the right term, since you are running a query against as database) and many other platform produces a programmed response designed to optimize for key performance indicators (KPIs) - i.e. click-throughs - ultimately feeding objectives of a paying client of that service - i.e. advertiser. The - often black-box AI-driven - polarization of online content and discussion is feeding yet another KPI: time-on-site, making the misdirection of user attention collateral damage in the pursuit of objectives set not by the individual but by the platform operator, and subsequently a 'marketer'. You never were a search engine USER, you have always been a Non-Player Character in a game designed by Google. This legacy of systems evolution and centralization can at the core be attributed to the implementation of database technologies, and consequent connection of these data siloes to networks including the internet. With the emergence of decentralized software solutions - such as blockchains and directed acyclic graphs - efforts are being made to mitigate against the hegemony of account-based systems, and return ownership of digital data and activity to the individual. As such, it is important to note that the true genius of the Bitcoin Whitepaper was indeed not the creation of a new "currency" -  a somewhat unfortunate mislabeling (more ) - but sovereign control over a set of bytes by a user, without the need for a centralized database authority. here Mark Zuckerberg, 2004 Self-Sovereign Identity and Other Metaphors The European 'Digital Identity Wallet' proposed by the EU’s executive commission is a smartphone app that would let users store electronic forms of credentials and other official documents, such as driver’s licenses, prescriptions and school diplomas, is the latest attempt in a long-line of failures to turn into viable technology. metaphors Machines do not forget, and data digitally recorded is almost immediately copied and distributed. It is therefore a naïve attempt to invoke 'Rights To Be Forgotten'.  - Humans have the unalienable right not to be observed! Human Agency Human agency refers to the human capacity to make decisions and enact them on the world, including machines and technologies. Human agency entitles the observer to ask in a way that would be nonsensical in circumstances lacking human decisions-makers -i.e. the impact of weather conditions.  If a situation is the consequence of human decision making, persons may be under a duty to apply value judgments to the consequences of their decisions, and held (legally) responsible for observable actions of those decisions. should this have occurred? Engineering for humans Having rummaged through dozens of design papers on "decentralized identity" for more than six years, a common threat emerges: engineers introducing the designation to describe operational aspects and objectives of technology, consistently fail to establish a of the term before committing the concept to source code. Developers seem content to appropriate and combine the syntax from various disciplines in a futile effort to obtain clarity of nuanced legal, and behavioral concepts. identity functional definition Despite disagreements among scholars of social sciences, philosophers, and psychologists about many facets of identity, a general consensus about its uniquely human quality exists. Further separating human beings from objects and other living organism is the ability to self-reflect and form intentions. Aside from situational and/or reflexive expressions, the basis for the latter is most often rooted in the sum of experiences, and memories thereof. Baring significant advances in human brain-monitoring technologies, engineering efforts serving human intentions thus far are limited to levels of observance and interpretation. In so far as this threshold is breached by technical implementations - i.e. software-assisted "social engineering" (a form of "hacking" human-assisted processes), definitions must allow for the attribution of human agency. At the time of this writing no there are known technologies addressing identity management . Solutions using the term "identity management" can readily be classified by the type and purpose of data collection and management: i.e. identification, including (for) access management and authentication, profiling, certification, etc. The often used term ' is a figure of speech frequently applied to a collection of personal identifiable data.  As with all metaphors it is not literally applicable to the design language of technology. Frequently the term can be replaced with an accurate designation such as or . The former aspect of the data is the subset available to the "user", while the latter is the totality of data collected and augmented by the the entity controlling the network or platform. digital identity' persona profile Metaphors are to be narrowly watched, for starting as devices to liberate thought, they end often by enslaving it. - Cardozo During the largest gathering of 'identity management' companies at KNOW, in Las Vegas, it was obvious that every participant had their own definition of that term "identity", and little interest in refining it to have intellectually honest discussions. Identity and Networks Below is a simplified visualization of layers surrounding human activity as seen through current network architecture. Importantly these layers must be considered both in the context of networks, and without any technology at all. Human activity can be divided into thoughts and emotion, as well as physical expressions. Discounting elaborate implementations such a functional magnetic resonance imaging machinery, only the latter expressions are generally available for observation. However, it is important to note that any observation of physical human activity regularly affects human thoughts and emotion  (observer effect). Considering that identity is formed by a process of (self-) reflection, observations must first provably pass conscious human approval processes. These paradigms which are regularly enforced in the physical world, can be returned to individuals via the use of cryptographic primitives which disallow, and/or invalidate copies. Just like border-agents do not get to keep copies of government credentials, control over online credentials must be implemented as digital bearer instruments. We are currently working on a taxonomy for identity engineering (see ). if you have a suitable legal or digital forensics background and want to contribute. Github Contact me *We are . If you think, you have a solution to the outlined problems . blockchain investors connect to me Interview on 'Privacy Investing':

Identity Management Unchained - Taking the Metaverse Red Pill

Technology and machines are becoming more and more like us as they advance. But this mimicry is already crossing over to our darker prejudices, adopting the same biases that have plagued society for so long. Machine learning, which is the most rapidly advancing application of AI, is today utilized everywhere from pointlessly fun app features like Snapchat filters to gravely consequential applications like law enforcement. The future isn’t far when decisions of political and social import will be made by AI assistants. Nonetheless, I can’t help but question the wisdom in placing more trust in machines than we can on a plain old-fashioned human being occupying high office. Not, at least, until we continue training AI algorithms with that compound gender and racial stereotypes, reinforcing the privilege of those established at the top of the social order. lopsided datasets And herein lies the danger of designing technology with complete disregard to its social repercussions. Gender bias and racism in machine learning data It sounds strange to suggest that an algorithm can be stereotypical and show preference towards certain groups in its complex computations. Does it even make sense to challenge the cold objectivity of mathematical algorithms and have the audacity to hold them guilty of biased judgement? It turns out that it makes perfect sense. In fact, we have all the reason to doubt the impartiality of our technology. Let’s take a look at a few examples: A team of researchers from the University of Virginia found that image datasets used for training machine learning algorithms are characterized by gender bias, containing predominantly male images. Females are outrageously underrepresented in the Microsoft and Facebook cosponsored image dataset as well as University of Washington’s COCO ImSitu The researchers found a classic case of gender stereotyping when a visual recognition algorithm trained on ImSitu mislabeled a man standing in a kitchen as “woman”. But sexism isn’t the only sin sophisticated machines are guilty of. In another research on software that predicts criminal recidivism, it was found that the algorithm gave to black people two times more than white people. A black woman with four juvenile misdemeanors was labelled “high risk”, while a white man with two prior armed robberies was labelled “low risk”. Surely enough, the lady didn’t reoffend, but the man went on to commit a grand theft. high re-offending risk scores Similar racial and gender discrimination is of Microsoft and IBM, which are much less accurate at identifying black women as compared to white men. found in face recognition software So, what’s going on here? To put it simply, machine learning algorithms are dependent on the datasets used to train them. If the data is skewed, the software will exhibit a preference towards the group that has majority representation in the dataset. Tech companies are simply feeding their own biases into machines. The above cases reveal only one of the two ways that machines can be made to discriminate unfairly. These are all instances of datasets being used to train algorithms. But what if the algorithm itself is biased? bad good If you’re looking for an example of such an algorithm, look no further than Google’s search engine. The bias of Google Search “Asian girls” on Google, for instance, and the search results will push scantily clad Asian women in seductive poses on top of any other context Asian girls can be found in. The same goes for Latinas. (Such search queries used to lead straight to pornographic content, but it bears mention that Google has suppressed much of it now. Nonetheless, a good chunk of search results associated with these queries still lead to hypersexualized representations of women of color). Google uses predictive technologies to anticipate user intent when a search query is typed. Now, I won’t be surprised if most people actually type in “Asian girls” with sexual amusement as the intent, but Google has a responsibility to display results in a balanced context, rather than blindly . fueling racial stereotypes Correcting these issues might entail a modification of the existing Google algorithm, which at present considers some 200 factors when generating search results for a query. Showing search results untainted by abject discrimination without also undermining the accuracy of comprehending user intent will probably be a challenge, but it is one that deserves Google’s immediate attention. As it stands, rewarding the privilege of majority on a platform as ubiquitous as Google is a disservice to the struggle for a fairer and equal society. The case of Google illustrates quite well that both the data and the algorithm (or in other words, the interpretation of the data) need to be free from bias to refine accuracy and improve fairness in AI systems. The ghost of scientific racism In a way, the problem of discriminatory technology hearkens back to scientific racism. There’s an of good data leading to bad conclusions in research of comparative capabilities of different human races, which piqued the interest of a few influential scientists in the 19th century. interesting example Chief among these was the American physician Samuel Morton, the founder of craniometry. The professor was known to have the largest collection of human skulls at the time, possessing over 900 skulls from five different ethnic groups which he categorized as Caucasian, African, Native American, Mongolian, and Malay. From 1839 to 1844, Morton painstakingly conducted his research, measuring the sizes of these skulls and averaging the results for each ethnic group. He found that Caucasians had the largest skull size and Africans were the smallest. Since brain size was believed to be the sole factor that determines intelligence by the contemporary scientific wisdom of the time, Morton had just supposedly found a scientific basis for the intellectual superiority of Caucasians and the inferiority of Africans. At around the same time, a German anatomist, Friederich Tiedemann, was performing the same kind of craniometric experiments to see how the skulls of different races relate. Curiously, this man reached an entirely different conclusion. Noting the significant overlap between skull sizes of all the measured races, it was obvious to Tiedemann that no significant difference exists between cranial capacity of different ethnic groups, and thus, there are no scientific grounds for racism. The important point to note here is that the data acquired by both scientists is almost similar and scientifically sound, but it led these men to diametrically opposite conclusions. If Tiedemann focused on averages, he would be tempted to make the same conclusion as Morton. But the measure that seized Tiedemann’s attention was the ranges of skull size for each racial group rather than the averages of each, which happened to be the sole focus of Morton. This simple change in interpretive approach was clear evidence to Tiedemann of the injustice of slavery and oppression of black populations, while Morton submitted to his possible inherent racist convictions when pronouncing all Africans inferior to Caucasians. If there’s something to take from this case and apply it to our present scenario, where the interpreters and decision-makers are machines, it is to purge our algorithms free from bias. Otherwise modern technology will only be perpetuating dangerous prejudices akin to scientific racism. Where do we go now? The problem with our misbehaving machines, as I see it, stems indirectly from the inequality of social structures around the world, and directly from the lack of diversity in leading tech companies i.e. Google, Facebook, Apple, and Amazon. It isn’t surprising to see that Google has the out of all major tech companies. In fact, female and minority representation in the tech industry as a whole is abysmally low. worst black representation The lack of diversity promotes an environment devoid of any checks against the transmission of discriminatory beliefs (conscious or unconscious) into the products these firms churn out. Anima Anandkuma, a Professor at California Institute of Technology who has previously worked on Amazon’s AI systems says, “Diverse teams are more likely to flag problems that could have negative social consequences before a product has been launched.” There is arguably no other industry that is more impactful to society than the technology sector. If the firms that comprise this industry are almost entirely represented by white male populations, it is hardly surprising that their products end up being biased against other ethnicities. As long as tech firms are monopolized by homogeneous groups of people with little to no diversity among them, biases are going to creep their way into the systems and software these firms create. It is not easy to fathom all the social implications of biased machines. But if there is one thing we can say about society of the future, I bet that it will be heavily influenced by technology with Google, Facebook, and Amazon being the key shareholders in this world. Marching forward into a future where machines are wired to exhibit stereotypical biases could be calamitous and serve only to accentuate the social polarization and inequality in modern society. To err is human, and that is exactly why we need technology. I just hope that propensity for error remains confined to the human race, for our sake.

When machines know sin: the algorithmic bias of technology

I Compiled a List of Tech’s “Next Big Things” So You Wouldn’t Have to

Earlier in my career, about 10–15 years ago, digital experiences (at that time, just webpages) had what was known as the “eight-second rule,” meaning an eight-second load-time max per webpage. At the time, eight seconds was about as fast as you could get. But today, the aspirational load time is more like one sub-second. Anything that takes longer is going to drive users elsewhere. Immediacy is the new normal. Part of the reason for this shift is obvious: Technology is more advanced, and we now have the ability to reach more consumers on more devices than we previously could. But the other part of this equation may be less readily-apparent. Consumer expectations have evolved. Immediacy isn’t a nice to have. It is a must-have. So, retailers, publishers, and brands must now provide content in real time. This shift has largely been driven by social media. Today’s internet users are accustomed to opening their Facebook, Twitter, or Instagram feeds and seeing fresh, personalized content updated moment-to-moment. In light of this, decisioning engines now need to decide what to show a customer or reader in less than one second. is no longer on its way — it’s a fact of the digital age. To keep up, online publishers must assess readers’ preferences moment-to-moment and provide the most relevant content available. And they need to try to keep users on their own sites, rather than driving users off to third-party sites. Real-time marketing Here’s what online publishers need to know: Machine learning helps ensure that your content adapts to reader preferences in the blink of an eye. The other day I took my son to see the Marvel movie . In the movie, a space alien arrives on Earth and can only survive by combining with a human host. The alien unites with a human and creates this amazing fusion: The human is only powerful with the alien’s power and the alien can only survive on Earth with the human. Venom Similarly, savvy tech leaders are partnering with machine learning technology to enhance customer experiences across a range of platforms. Real-time marketing is a departure from the status quo approach to personalization, which involves making suggestions based on a users’ and/or on privacy-intrusive profiling. declared preferences For example, when you sign up for a media site, you’ll likely be asked what topics you’re interested in. (This might be politics, business, or pop culture, for example.) But often, what people actually click on ends up being vastly different from their stated preferences. This means that publishers need to be aware of the disconnect and suggest content accordingly. This is the user’s which are perfect for a machine learning system to detect and make content decisions based upon. observed preferences, Readers’ preferences change moment-to-moment. This is unlike an online store where the user is typically going to buy something and may look at a related product. In the case of online media, the reader will be a target for a wide range of content. Picking up on various signals that the user gives off in the moment to determine their interests is critical, while also leveraging external factors such as stock trends, weather, news events, or trending articles can also help generate relevant recommendations. The future of online publishing is all about understanding what readers want in the moment. If you don’t have a super-responsive site that can pick up on trends, you should consider implementing technology that picks up on readers’ individual preferences in real time. Otherwise, you’ll lose them. Send readers to native content on your site — but do it in real time. If you’re able to capture a reader’s interest and suggest trending articles at the bottom or side of the page, that’s a solid start. But any recommendation service that’s sending readers to a different site has several flaws. First, while the publisher might perhaps make some ad revenue, they will be losing that user for at least a moment — maybe that day, or even forever. From a customer service standpoint, it’s always preferable to drive users to more content on your site as opposed to someone else’s. On a recent trip to New York, I met with a major news media company who confided that they’d been losing website viewers since they can’t effectively respond to real-time trends across their entire site. Here’s what happened: They got a scoop on a story and published it on their main site. As expected, the main site was getting a lot of clicks and was driving traffic from search engines to their site to find the article. But they still had a problem with getting that hot content recommended at all locations across their site to aid in . recirculation Their existing system could not react in time to put the scoop on the other pages of their site. And you can’t capitalize on a trend if you’re not making sure it’s not just prevalent on your homepage but everywhere on your site. To do this, you need to have a way to make sure your site is fully dynamic and responsive. If you aren’t, you’re missing out on a major business opportunity. If nothing else, make sure your company values align with the content you’re linking to. When you drive traffic to another site you have no control over the quality of that content. It could be good content, or it could be horrible. Either way, that experience will reflect on the brand of the referring site. And if that content doesn’t align with your core values, you’re losing brand affinity. And that will hurt you in the long-term. When you leverage a real-time marketing solution on your site, you can showcase your brand’s ideals and values by aligning your brand with certain causes valued by your customer base. Nike is a great example. This fall, Nike designed a campaign around , the NFL player famous for refusing to kneel during the national anthem at games. Despite a major boycott, the campaign earned Nike dollars in additional revenue. Colin Kaepernick $6 million When you know what your readers are seeing, it’s easiest to deliver content you can vouch for. Anything else is a risk for your brand. In the real-time age, publisher sites need to keep up with the customer expectation that the content is going to be just as relevant and immediate as what they are accustomed to on their social media feeds. Pretty soon, readers won’t settle for less. This piece was originally published on DCN .

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