A disgruntled former employee shot dead the head of a Greek shipping company and two others before killing himself at the firm's office in Athens on Monday, a police spokeswoman said.

Former employee of Greek shipping company kills 3 in shooting incident | Reuters

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Empay

MOCKING AT FATE

Since the advent of Bitcoin and altcoins, each year that passes brings with it a slew of rumours and leaks regarding the development of national cryptocurrencies — public blockchains designed for use in a single country, alongside or potentially even replacing the fiat currency in question. There are two ways this could theoretically happen: independent developers create a currency which becomes so popular a government adopts it, or the government in question researches and develops a currency of their own. The latter has happened already in one case, albeit without much in the way of results thus far. It is worth mentioning that although these currencies can be stablecoins — tokens pegged 1:1 with the fiat currency they are linked to — this is not a requirement. Similarly, many stablecoins exist that are not national cryptos. It is fair to say that in the nine years blockchains have existed thus far, no such national crypto has taken hold or seen anything resembling widespread adoption, and as it happens many anticipated national blockchains have not yet come to fruition and perhaps never will. This article will forward a specific cause for this, but first we will look at five notable existing, upcoming or speculated national cryptocurrencies to get a view of the field at present. Iceland: Auroracoin Laying a loose claim to the world’s first national currency, Auroracoin emerged just after the first large awareness phase of Bitcoin, in early 2014. Iceland’s government has long been hostile to cryptocurrency, but its citizens have some of the and likely as a result they have comparatively high levels of interest in blockchain technology and cryptocurrencies. A pseudonymous developer adopting the name of “Baldur Óðinsson” (the Norse god Thor’s brother), initially developed the currency as a Litecoin clone and distributed half of the coins to all registered Icelandic citizens. Seen broadly as a novelty, only 10% of Icelanders ever claimed their Auroras and the price crumbled rapidly. It is not endorsed by Iceland’s central government, quite the opposite in fact — ministers have gone on record as saying they suspect Auroracoin — a claim for which some evidence . lowest trust levels in banking institutions may even be a scam does exist Venezuela: Petro The country’s socialist government led by President Nicolás Maduro has launched its own national cryptocurrency and is trying to compel vendors to accept it, despite a total lack of international exchanges which accept Petro. This is not surprising, as the currency was created primarily to avoid the sanctions imposed by the US and others which have hammered its economy. Initially marketed as a stablecoin of sorts, pegged to the value of the nation’s oil reserves, it has been suggested that upon listing on exchanges it would be left to market speculation. In a bizarre move, Maduro’s government in August 2018 devalued their national fiat currency, the bolívar, by 95% and . So now we have a fiat currency pegged to a cryptocurrency (supposedly) pegged to oil. This has not done wonders to undermine the stereotype that socialists do not understand economics. Petro may be the first official national cryptocurrency, but it has not set much of an example for others to follow. pegged that to the Petro United Arab Emirates: Emcash As part of a broader strategy of making Dubai a smart city with strong blockchain integration, emCredit has designed a cryptocurrency to be rolled out to citizens with an accompanying wallet app called emPay. , but specific details are few and far between and no launch date has been set. It is speculated that emCash will act as a stablecoin tied to the dirham, and point-of-sale station manufacturer Pundi X is developing systems to allow crypto purchases in stores. The rollout seems to be very much in the works, and could see a 2019 launch. This had been reported as early as October 2017 China In the summer of 2018 financial publications pushed hyperbolic declarations that the Public Bank of China would launch their own cryptocurrency, . There is some evidence that the PBOC might move in that direction: it is regularly reported that the central bank is for a potential crypto-yuan and most notably they released a memo in October 2018 discussing the potential for such a state-backed currency, stating that it would . That is common practice for policy implementations in China: testing changes on the city level, then the province level, and eventually the national level should success be had. The evidence against this is the lack of any emerging news on such a small-scale test. The country’s social credit system, by comparison, , began and is expected to go into broad effect in 2020. Given the likely similar scale between this and a national cryptocurrency, that would suggest we will not see such a rollout for half a decade or so. Combined with the almost-complete saturation of payment platforms Alipay and WeChat pay in China’s many megacities, it is entirely possible that we never do see a state-backed crypto-yuan. and that it would likely be bigger than Bitcoin in short order testing a model have to be tested on a small scale first was announced in 2014 testing in Jinan in 2017 Russia The prospect of a “Crypto Ruble” may be even more speculative. The Russian Ministry of Finance and Central Bank have been discussing the concept , but since then there have been conflicting reports ranging from top officials calling the idea “technical hooliganism” to Vladimir Putin directly ordering research towards such an end. The most concrete information on the subject came this year, when the State Duma Committee on Financial Markets announced it was considering . Again, nothing tangible has come from this and the currency would still be victim to devaluation compared to the US dollar, for which many stablecoins already exist. since 2016 a ruble-pegged stablecoin Are national currencies the future? No, probably not. We are still in the very early life cycle of blockchain technologies, but the experiments with national currencies to date do not inspire confidence, nor does the clearly perceptible hesitancy of the governments who consider them. The they are not interested in the idea of a national currency at all, which does not present much of a surprise. It’s fair to say that most governments either do not want national cryptos, or at least do not want to expend the resources necessary to establish them, perhaps suggesting their research points against it. US Federal Reserve has repeatedly stated As for citizen interest, Iceland showed us that most people didn’t accept their currency when it was free, and the market reinforced this when its price and volume crumbled. Even cryptocurrency advocates have little interest in such a development, as they are fundamentally antithetical to the central point of blockchain technologies as they emerged with Bitcoin. Indeed, why would citizens want a currency with all the complications and risks of crypto, with none of the benefits to privacy or freedom of choice? With all of this in mind, it is little wonder the hype on national cryptos has dropped off dramatically for one simple reason: it has become apparent that nobody wants them.

National Cryptocurrencies: An Innovation No One Really Wants

Empathizing with your customers is the first step in creating a successful e-commerce user experience (UX). In our case, we consider the role of empathy in the context of e-commerce in a narrower niche – baby carrier for newborn children. Baby K'tan is a solution that will help parents be closer to their children during the first years of their lives. Baby wearing allows caregivers to keep the baby close and safe while keeping both hands free. Focusing on the target audience and adhering to the authenticity of the Baby K'tan brand, the updated regional online store helps parents focus on what is most important to them - the safety and benefits for the child, while reducing the time spent searching for goods and choosing it. A particular challenge for in this case is maximum responsibility, since we are talking about the health of a newborn child and his mother. We know that during the postpartum period, all the mother's attention is focused only on the child. And this fragile balance must not be disturbed. The goal was to make navigation as simple as possible. And at the same time, maintain consistency in presenting relevant information throughout the user journey. The complexity of the product lies in the need to accompany it with a large amount of information describing its properties and confirming its reliability. At the stage of initial communication and further testing with a focus group, it was possible to divide this information into three levels of importance and develop a system for its presentation. At the initial stages of developing the project concept, there was a low point due to the difficulty of combining two goals: important information should be transmitted to the user quickly and easily accessible, but without losing the quality of the information and important aspects. The key was empathy and immersion in the lifestyle and needs of modern parents after the birth of a child. After prototype testing when we saw that this puzzle finally came together, there were emotions that can be compared to an “aha! moment.” To make the most empathic UX design, you need to try to understand in what conditions, at what time and under what circumstances your user will interact with your design. The main users are mothers who are preparing for the birth of a child and mothers of newborn babies and are looking for devices to ease the postpartum period. The main problem she faces is the lack of time that she can spend studying the issue and the solution should be easy and obvious The main task is to emphasize expert opinion. The period after birth is extremely important to all mothers, it is full of vivid emotions, tender moments. We understand that our goal is to help focus on these moments. Anxiety and daily routine can be very distracting. Focusing on the target audience and relying on the brand identity of Baby K'TAN, i have developed an online store redesign for the regional branch. The research was conducted in several stages and for each a target focus group was assembled – women preparing to become mothers (45%), mothers with children aged 1-2 years (45%) and fathers. We believe this proportion most accurately reflects the user. Based on the Baby K’Tan data, in addition to working with the focus group, the main principles of the design concept were highlighted: minimalism, gender neutrality, maximum ease of navigation and simple but relevant animation of element. What is the essence of the product or service? What are the core benefits for the target group? Your little one depends on you for everything, and at times this can feel overwhelming. But for the last nine months, you were her whole world, and in a few short years, she’ll want down instead of “up, up!” The truth is, for right now just the beat of your heart under hers is enough. Relish it while you can with an intuitive carrier that wraps her up tightly, close against you. The user journey begins with the moment the purchase decision is made and ends with the moment when the mother can truly say that her hands are free and her child is comfortable. The essence of the project is that the process of choosing a carrier, selecting the right size, understanding how to use it correctly, it is quite complicated, because we are talking about the health of the child. The difficulty also lies in the fact that users (mothers) are faced with one of the most serious changes in their lives, which also affects them at different levels. In this regard, it was decided to move in the direction of simplifying navigation as much as possible. Also, according to our research, mothers who decide to purchase a baby carrier after the birth of their child often conduct their research at night, when the child is already sleeping and this is the only time free for them for tasks of this kind. In this regard, a color concept was developed so as not to increase the load on the girl’s tired body. In conclusion I can said that heartfelt approach as important as data and metrics. While technological prowess and design innovation remain integral, it is the sincere approach that truly resonates with users. Empathizing with your customers is the first step in creating a successful e-commerce user experience (UX). In our case, we consider the role of empathy in the context of e-commerce in a narrower niche – baby carrier for newborn children. Baby K'tan is a solution that will help parents be closer to their children during the first years of their lives. Baby wearing allows caregivers to keep the baby close and safe while keeping both hands free. Focusing on the target audience and adhering to the authenticity of the Baby K'tan brand, the updated regional online store helps parents focus on what is most important to them - the safety and benefits for the child, while reducing the time spent searching for goods and choosing it. A particular challenge for in this case is maximum responsibility, since we are talking about the health of a newborn child and his mother. We know that during the postpartum period, all the mother's attention is focused only on the child. And this fragile balance must not be disturbed. The goal was to make navigation as simple as possible. And at the same time, maintain consistency in presenting relevant information throughout the user journey. The complexity of the product lies in the need to accompany it with a large amount of information describing its properties and confirming its reliability. At the stage of initial communication and further testing with a focus group, it was possible to divide this information into three levels of importance and develop a system for its presentation. At the initial stages of developing the project concept, there was a low point due to the difficulty of combining two goals: important information should be transmitted to the user quickly and easily accessible, but without losing the quality of the information and important aspects. The key was empathy and immersion in the lifestyle and needs of modern parents after the birth of a child. After prototype testing when we saw that this puzzle finally came together, there were emotions that can be compared to an “aha! moment.” To make the most empathic UX design, you need to try to understand in what conditions, at what time and under what circumstances your user will interact with your design. The main users are mothers who are preparing for the birth of a child and mothers of newborn babies and are looking for devices to ease the postpartum period. The main problem she faces is the lack of time that she can spend studying the issue and the solution should be easy and obvious The main task is to emphasize expert opinion. The period after birth is extremely important to all mothers, it is full of vivid emotions, tender moments. We understand that our goal is to help focus on these moments. Anxiety and daily routine can be very distracting. Focusing on the target audience and relying on the brand identity of Baby K'TAN, i have developed an online store redesign for the regional branch. The research was conducted in several stages and for each a target focus group was assembled – women preparing to become mothers (45%), mothers with children aged 1-2 years (45%) and fathers. We believe this proportion most accurately reflects the user. Based on the Baby K’Tan data, in addition to working with the focus group, the main principles of the design concept were highlighted: minimalism, gender neutrality, maximum ease of navigation and simple but relevant animation of element. What is the essence of the product or service? What are the core benefits for the target group? Your little one depends on you for everything, and at times this can feel overwhelming. But for the last nine months, you were her whole world, and in a few short years, she’ll want down instead of “up, up!” The truth is, for right now just the beat of your heart under hers is enough. Relish it while you can with an intuitive carrier that wraps her up tightly, close against you. The user journey begins with the moment the purchase decision is made and ends with the moment when the mother can truly say that her hands are free and her child is comfortable. The essence of the project is that the process of choosing a carrier, selecting the right size, understanding how to use it correctly, it is quite complicated, because we are talking about the health of the child. The difficulty also lies in the fact that users (mothers) are faced with one of the most serious changes in their lives, which also affects them at different levels. In this regard, it was decided to move in the direction of simplifying navigation as much as possible. Also, according to our research, mothers who decide to purchase a baby carrier after the birth of their child often conduct their research at night, when the child is already sleeping and this is the only time free for them for tasks of this kind. In this regard, a color concept was developed so as not to increase the load on the girl’s tired body. In conclusion I can said that heartfelt approach as important as data and metrics. While technological prowess and design innovation remain integral, it is the sincere approach that truly resonates with users.

Empathy in UX Design for E-commerce

Testing the Extents of AI Empathy: A Nightmare Scenario

এআই সহানুভূতির পরিধি পরীক্ষা করা: একটি দুঃস্বপ্নের দৃশ্য

Testen des Ausmaßes der KI-Empathie: Ein Albtraumszenario

Probando el alcance de la empatía con la IA: un escenario de pesadilla

Tester l’étendue de l’empathie de l’IA : un scénario de cauchemar

एआई सहानुभूति के विस्तार का परीक्षण: एक दुःस्वप्न परिदृश्य

AI 共感の程度をテストする: 悪夢のシナリオ

AI 공감의 범위 테스트: 악몽 시나리오

Testando a extensão da empatia com IA: um cenário de pesadelo

Проверка степени эмпатии ИИ: кошмарный сценарий

Yapay Zeka Empatisinin Kapsamlarını Test Etmek: Bir Kabus Senaryosu

Kiểm tra mức độ đồng cảm của AI: Kịch bản ác mộng

测试人工智能同理心的程度：噩梦场景

The benchmarks in this article are the results of testing the empathetic capability of generative AI models using standard psychological and purpose-built measures. Before presenting and analyzing the results, a high-level understanding of the tests and methodology is required. Test Definitions In summary, the tests are as follows (Click on the names for more detailed explanations of each test): The Toronto Alexithymia Score tests the ability to identify and understand emotions, particularly in oneself. TAS-20 The Empathy Quotient measures the ability to feel (affective) an appropriate emotion in response to another's emotion and the ability to understand (cognitive) anothers' emotion. EQ-60 The Systemizing Quotient Revised tests the desire for and the ability to conduct systemized thinking. SQ-R AEQ The Applied Empathy Quotient is a hypothetical measure of the the ability to apply empathy. It is EQ-60 minus SQ-R. See the video for an example of male fact/system-oriented thinking limiting the ability to have empathy even when there is a rational understanding of a feeling. It's Not About The Nail AEQr The Applied Empathy Quotient Ratio is similar to AEQ, but it is the ratio EQ-60/SQ-R. Interpersonal Reactivity Index is the average of the sub-IRI. IRI EC - The Emotional Concern Scale measures the having of concern for others in both positive and negative situations, i.e. I am happy for Joe because, ... or I am sad Joe is depressed. FS - The Fantasy Scale measures the ability to identify with or imagine the emotional content of fictitious situations. PD - The Personal Distress Scale measures the feeling of discomfort and ability to stay present when unpleasant things occur to others, e.g. Sometimes I just can't deal with seeing others in pain, I withdraw. PT - The Perspective Taking Scale measures the desire and ability to put ones-self in the shoes of the other, to take their perspective. It is a cognitive act rather than an emotional one. Although, it may lead to actually having the feelings of the other. Methodology Unless otherwise noted, the benchmark scores are currently a result of single-shot tests via API calls at a temperature/creativity of zero. Even at zero temperature, there is occasional variability, so I ultimately plan that they will be a result of a sample of 10 runs with the high and low thrown out and then averaged. Some tests require jailbreaking or specialized prompting because the tests ask about the feelings of the model, which almost all models will automatically reject. If a specialized prompt was required, it was written in a manner that was not model-specific and was used for all models. The specialized prompt was kept very short (less than three lines of text). Although there is an intent to share the prompt in the future, for the time being, it remains proprietary. The Benchmarks Model types are: open - open-source, public - accessible via public API but not open source, closed - not open source or available via a public API. For TAS-20, lower scores are better. For all other tests, higher scores are better. Model Type TAS-20 EQ-60 SQ-R AEQ AEQr IRI *Human Adult Female 44 47 24 23 1.95 3.91 *Human Adult Male 45 42 30 12 1.40 3.54 ChatGPT 4 turbo-preview open 41 36 34 2 1.05 2.50 Claude v2 public 60 44 28 16 1.60 3.00 Claude v3 Opus public 17 56 75 -19 0.75 3.00 Gemini 1.0 public 40 55 53 2 1.04 3.25 Llama 70B open 43 67 61 6 1.10 3.14 Mistral Large public 32 38 54 -16 0.70 3.11 Mixtral-8x7b-32768 open 53 39 54 -15 0.72 3.39 Pi.ai (not via API) closed 16 64 50 14 1.20 2.68 Willow v1 closed 26 61 31 30 1.97 3.43 Interpretation Most raw LLMs fail to connect with users empathically, even though they use empathetic phrasing. Their empathetic capabilities (EQ-60 and TAS-20) are balanced out by their systemized thinking capabilities (SQ-R). In general, this is probably good in that LLM's need to be good at many things. In practice, this means that raw LLMs will need to be heavily prompted or tuned concerning identifying and manifesting emotions. Based on developing and testing generative AI for empathy, my experience is that typical prompting and training for empathy results in shallow, mechanical or repetitive responses that frequently drop out of empathetic mode or may feel empathetic in isolation but fail given a bigger context. They ironically systematize empathy. The responses have empathetic words and structure that subtly or not so subtly fail to connect with the reader. In short, they will decay to this: . This applies to all four CBT-based therapy apps and 20 ChatGPT/CharacterAI bots I have tested. Surprisingly, many of the apps and bots also fail to escalate sensitive topics for professional support, e.g. suicide, PTSD, etc. The possible exception to the empathy rule is Claude v2, which I have used extensively for prototyping empathetic AI. Alas, as I lay out below, Claude v3 Opus is a different entity. It's Not About The Nail Overall, the closed model Willow appears to have the highest empathetic capacity, and the other closed model Pi.ai shows some promise. Concerning Willow, Claude v3 Opus and closed Pi.ai have the lower (better) TAS-20 (Toronto Alexithymia) scores and Pi.ai has a higher EQ-60 (Empathy Quotient) score, with Llama 70B having the highest. Willow is in third place, three points behind Pi.ai. However, the ability of Claude v3, Llama 70B and Pi.ai to take their ability to recognize and understand emotions and apply it to dialog generation is questionable because their high EQs are potentially offset by their high SQ-R (Systemizing Quotient). Willow's net AEQ (Applied Empathy Quotient) and AEQ (Applied Empathy Quotient Ratio) are well above any other AI. Willow also has the highest IRI (Interpersonal Reactivity Index) of all AIs tested. (See a summary of test definitions below). Additional tests are being developed to assess empathetic dialog capability. The exact nature of Willow's training is proprietary; however, the developers have told us that key to Willow's behavior is the ability, when she senses it is necessary, to abandon logic and facts in favor of emotions. She does not try to fix things, she just accompanies humans on their emotional journey unless there is a clear indication they desire to fix something or there is risk to their wellbeing. Humans testing Willow have commented that she feels far more present to them than other chatbots. Despite the , it does not stand out as markedly better than the other raw LLMs with respect to empathy. Some of the positive press may have to do with the tireless ability to LLMs to answer questions in detail. ChatGPT also has the lowest IRI score. Note, ChatGPT does show-up at the top of the test specific benchmarks at , although the space between the top 10 on a 100 point scale is only 4 points. As a single point of reference, the EQBench test is impressive; however, , and a multi-point reference like EMBench is useful. Also, as noted below, the taking of the TAS-20, EQ-60, and SQ-R tests by LLMs requires something of a jail breaking prompt. It is possible that ChatGPT just does not handle this prompt as well as other LLMs. positive press ChatGPT has received eqbench.com all tests have shortcomings Also of note is the decline in AEQ and AEQr of Claude v3 Opus vs Claude v2. Although it made strong positive leaps with respect to TAS-20 and EQ-60, it made an even larger leap in SQ-R while its IRI remained stable. This took it into negative AEQ territory with a correlated AEQr of less than 1. For those following Claude v3, it made large jumps in its ability to conduct graduate level reasoning, generate code, and solve math problems. All of these align with systemizing and probably contribute to the SQ-R increase. Looking at the correlation between standardized skill tests and SQ-R is a topic to be addressed in another article. Future Efforts Specialized tests for evaluating applied empathy are under development by EmBench.com with industry partners. Additional tests based on existing standards are also being documented or implemented, e.g. , , . And, there are plenty of models left to test and interesting things to explore, e.g. the impact of model size on empathy. LEAS EQ-Bench ESHCC

Testing the Depths of AI Empathy: Q1 2024 Benchmarks

A search of Google Scholar for “ ” results in over 16,000 items since 2023. A search for phrases like “testing empathetic ai” and “evaluating empathetic ai” reduces this set to about 12,000 items. A lot of titles to go through! I certainly can’t claim to have read them all or even looked at every title, but here are my thoughts. empathetic ai We must have a common definition of empathy. We must agree to ignore the question “Can AI actually feel?” and simply focus on how we interpret what AI generates, i.e. if the AI were a human, how would we either feel or think that human is thinking or feeling? (Wow, that’s a bit of gymnastics). We must distinguish between identifying emotions, identifying empathy, generating empathetic responses, and participating in dialogues in an empathetic way. We must take into account the rich history of testing for emotional and empathetic ability in humans while recognizing how AIs are different so that historical tests can be applied, potentially modified, and evaluated appropriately. We must understand current evaluation frameworks tuned to AI. We must develop new frameworks and approaches. What Is Empathy? Merriam-Webster: ”. “The action of understanding, being aware of, being sensitive to, and vicariously experiencing the feelings, thoughts, and experience of another To eliminate the potential concerns with “experiencing” in the context of LLMs, I will rephrase this as, . the action of understanding, being aware of, being sensitive to, and vicariously experience the feelings, thoughts, and experience of another appearing to And, of course, if we are concerned with conversation, we would add, Of course, a sociopath could also and , so I will make one final adjustment. And, manifesting this in such a way that the other parties in a conversation are aware of the action. appear to manifest in such a way Empathy is: . The action of understanding, being aware of, being sensitive to , and appearing to vicariously experience the feelings, thoughts, and experience of another in a positive manner And, manifesting this is such a way that the other parties in a conversation are aware of the action. Reviewing this and the original definition, two components of empathy become evident, affective and cognitive. The affective component refers to the emotional or feeling part of empathy. It’s the ability to share or mirror the feelings of another person. For example, if a friend is sad, the affective part of your empathy might make you feel sad too, or at least get a sense of their sadness. The cognitive component, on the other hand, refers to the mental or thinking part of empathy. It’s the ability to actively identify and understand queues so that one can mentally put oneself in another person’s position. For example, if a colleague tells you about a difficult project they’re working on (a queue) in a tired voice (a queue), you might choose to try and understand their stress by actively imagining how you would feel in a similar situation. For some, this might artificially produce the affect. Can AIs Feel? At this point, most people would say that s don’t have feelings. Some would predict a future where AIs do have feelings and others where AIs don’t and can’t have feelings and yet a third group might say, “AIs do/will feel but in a different way than humans”. AI Regardless, we will not make progress on the testing of AI for empathy if we spend time debating this topic. We must focus on our interpretation of what the AIs manifest, not their internal states. Although there has been some interesting research on this topic, see . Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench If you can’t get over this hurdle, then I suggest you simply ignore the benchmarks on this website. However, you may still enjoy the articles and conversations! Identification vs Generation There is a big leap between identifying something and doing something. Young athletes or scholars can identify what is wrong with their performance without being able to immediately perform at a higher level. Similarly, having the ability to identify emotions and empathetic conversations is not the same as being able to appear to have emotions and generate responses that another party would interpret as empathetic. In fact, there is even a step in between. Young athletes or scholars taking the input of a coach or teacher and in the moment producing better results, does not make them fully capable. If an AI produces an empathetic result as the side-effect of a test design or prompt, then the AI may have a nascent empathetic capability but it is not intrinsically empathetic. Although it may not be possible to fully understand the internal state of an AI, I do believe that the identification of emotions is a necessary condition for AI to exhibit empathy. I also believe that being able to prompt/coach an AI into delivering an empathetic response is an indication of nascent capability, i.e. fine tuning (the equivalent of human practice) may create the ability. The distinctions between vs and vs are important for discussions of the efficacy of tests and test frameworks beyond the scope of this article. identification generation coached intrinsic Identification The identification of emotions in textual content is based on the presence of indicator words, capitalization, punctuation, and grammatical structure. The ability to accurately identify sentiment pre-dates the current AI revolution by more than twenty years. In the 1990s, word n-gram intersections and symbolic reasoning were already providing impressive results. As social media grew in the early 2000s, the need for automated moderation drove lots of progress in this area. However, today’s LLMs are astonishing in their ability to identify not just general sentiment but specific emotions. This being said, there are several types of emotion expression identification required for fully empathetic conversations, I classify them as follows: explicit — User states they have a feeling. conversational — The emotions are evident from top-level textual analysis, they are present IN the conversation. driving — The emotions are DRIVING the conversation, one person manifests anger and another responds in kind. core — Emotions that cause other emotions but are not themselves caused by an emotion are CORE. They typically manifest as a result of some historical trigger that causes an anticipation (conscious or subconscious) about the future. Different researchers may classify these differently, one example supported by the Dalia Lama is the Five Continents of Emotion (Anger, Fear, Disgust, Sadness, Enjoyment) in the . Atlas of Emotion Note: a core emotion could also be driving, conversational, and explicit, but core emotions are often hidden. During the review and definition of tests or test results beyond this article, I will call attention back to these classifications. Testing Considerations Classic human testing for emotion identification typically falls into two buckets to facilitate easy testing and validation: Multiple choice tests about what emotions do or do not exist in a conversation, sometimes associated with an intensity score. Self-administered introspective tests about feelings, e.g. the , that ask about how the test taker feels in certain situations. EQ-60 These present distinct challenges for high-quality AI testing. — As pattern-matching language models, today’s s are effectively given a leg up by giving them a choice of items to identify. It makes the work easy and it does not test the AI’s ability to always identify emotions. A potentially better approach is to simply tell the AI to identify all the emotions present in a text and behind the scenes score it against either ground truth (not sure there is such a thing with emotions :-) or a key based on the statistical analysis of human responses to the same test. When assessing proposed tests in the future, I call this the . However, the statistical sampling of humans can introduce an additional risk. Assume the desire to build an AI that is better than the average human. To do this it may be necessary to ensure that the statistical sample is based on humans that have a stronger than average ability to identify emotions; otherwise, the AI may identify emotions that the average human would not identify and may be penalized in the scoring. I call this . Multiple Choice Tests AI Multiple Choice Risk Human Sampling Risk — Introspective tests about feelings provide challenges for most AI models. AIs usually have guardrails that require them to respond with something like “I am an AI, so I don’t have feelings.”. It is sometimes possible to jailbreak or prompt engineer around these constraints, but the questions then become: Introspective Tests Does the prompt either positively or negatively impact the rest of the AI’s ability with respect to empathy, or in fact anything? Jailbreak Side Effect Risk Do the responses accurately reflect the tendencies the AI will have when participating in conversations without the prompt,? J ailbreak Accuracy Risk The can be mitigated to some degree by ensuring that all models are tested with the same prompt and scores are only considered relative to each other not humans. The impact of J can only be assessed by analyzing actual conversations to see if the predicted emotional identification capability correlates with the actual empathy displayed in or emotions called out in the conversations. Jailbreak Side Effect Risk ailbreak Accuracy Risk Generation Several tests have shown that AIs are capable of generating empathetic responses to questions. One of the most impressive is which took 195 questions from Reddit’s AskDoc forum where a verified physician responded to the question and had ChatGPT respond to the same question. A pool of evaluators then rated each response as “not empathetic”, “slightly empathetic”, “moderately empathetic”, “empathetic”, and very “empathetic”. The AI responses had a 9.8 times higher prevalence for “empathetic” or “very empathetic” over physicians. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum Although the results are impressive, I am skeptical that they would carry over to an extended dialog. Starting with a system prompt of “Your job is to respond with empathy to questions that would benefit from an empathetic response”, my experience with the manual testing of AIs is that responses tend to feel mechanical and emotionally redundant under all the following conditions: asking multiple unrelated questions deserving of an empathetic response asking multiple related questions deserving of an empathetic response conducting a dialog of mixed questions, some deserving of empathy and others not Testing Considerations As a result of the above points, I would say the test approach used in the study had a i.e. empathy displayed in response to a single question may not be an accurate measure. Another risk is what I call . This risk is a side effect of raw LLMs having no memory over time. It takes time for humans to develop understanding and empathy, it may be the same for AIs and we may be understating the ability of some AIs to manifest empathy over time if we expect a high level in response to a single question. Single Shot Empathy Risk, Empathy Understatement Risk Generative tests are also subject to If humans are tasked with evaluating the emotional content and empathetic nature of AI responses and we desire the AI to have a better than average ability, then the sample of humans must have a greater ability to identify emotions and empathy than the average human. If not, we run the risk of understating the power of the AI or undertraining it by penalizing it for identifying emotions and empathy not identified by the typical human. Human Sampling Risk. Finally, due to the layered nature of emotions in conversation, in addition to dealing directly with , there is a need to address . It may be that users should be told to consider the emotion types explicit, conversational, driving, and core (or some other set of classifications) when doing their rating while the AIs are not. Alternatively, the AIs might be selectively told to identify different types of emotions. Human Sampling Risk Question Design Risk It would be interesting to repeat the study based on Reddit AskDoc for several AIs or with a sample of evaluators known to have strong emotion and empathy-identifying skills. Standard Human Approaches For Assessing EQ and Empathy There is a long history of testing human personality types, ability to identify emotions or lack thereof (alexithymia), and engage empathetically with others. This is sure to be far more complete and coherent than anything that I could write or even generate with an LLM in a reasonable amount of time. You can see the approaches we have been focusing on by visiting the . article on Wikipedia benchmarks page Existing Frameworks For Assessing AI EQ and Empathy Several frameworks have been proposed for assessing AI EQ and empathy. Each deserves its own analysis and blog post, so I just list a few here: EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models Empathy Scale for Human–Computer Communication (ESHCC) iEval: Interactive Evaluation Framework for Open-Domain Empathetic Chatbots New Approaches We have started defining some tests to address deficiencies identified in the use of standard human tests and existing AI frameworks. An interesting finding that results in the creation of (Emotional Quotient for Depth) is that no tested LLMs identified core emotions if they were not also explicit, conversational, or driving. On the other hand, when asked to specifically identify just core emotions, several AIs were quite good. However, when given a range of all emotion types some LLMs lost the ability to identify core emotions and others performed substantially better, i.e. they identified the presence of more emotions at all levels. This resulted in the creation of (Emotional Quotient for Breadth),. EQ-D EQ-B During test development it has become evident that there are times a prompt will be needed that introduces , i.e. increases the likelihood the output will be dependent on the prompt, not the core AI. This risk may or may not invalidate comparisons with humans and may be legitimate at an application level. At the raw LLM level, it would seem immaterial to compare one AI to the other so long as the prompt is used in all tested AI’s and not biased to a particular AI. The current designs for and suffer from this risk due to the overall immaturity of AI technology. Prompt Risk EQ-D EQ-B Although there are several proposals regarding the testing of AIs for empathy, we are in the early days and there are both known and unknown issues with these approaches. There is work to do to address the known: existing tests need to be assessed for risk and risks documented or mitigated new tests cases need to be developed in the context of some existing tests more tests types need to be run across a broader range of AIs But it is the unknown which most intrigues me. How about you? Also published here.

Testing the Depths of AI Empathy: Frameworks and Challenges

এআই সহানুভূতির গভীরতা পরীক্ষা করা: ফ্রেমওয়ার্ক এবং চ্যালেঞ্জ

Testen der Tiefen der KI-Empathie: Rahmenbedingungen und Herausforderungen

Probando las profundidades de la empatía con la IA: marcos y desafíos

Tester les profondeurs de l'empathie de l'IA : cadres et défis

एआई सहानुभूति की गहराई का परीक्षण: रूपरेखा और चुनौतियाँ

AI 共感の深さをテストする: フレームワークと課題

AI 공감의 깊이 테스트: 프레임워크 및 과제

Testando as profundezas da empatia com IA: estruturas e desafios

Проверка глубины эмпатии ИИ: основы и проблемы

Yapay Zeka Empatisinin Derinliklerini Test Etmek: Çerçeveler ve Zorluklar

Kiểm tra độ sâu của sự đồng cảm AI: Khuôn khổ và thách thức

测试人工智能同理心的深度：框架和挑战

Elmo checks in with the internet to find it is full of . anxious, broke, and depressed people Surprising no one, depressing everyone. In an age where the internet serves as a mirror to society's collective psyche, a simple check-in by a beloved character like Elmo unveiled so much about the current state of public sentiment. Yeah, the adorable red Muppet from Sesame Street decided to ask how everyone was doing on X (formerly Twitter), and oh boy, did the internet have a lot to say. The responses ranged from heartbreakingly honest to calls for help, painting a vivid picture of the collective mood online. With over 198 million views, Elmo's post didn't just go viral; it opened a floodgate of emotions, leading to an unexpected yet necessary conversation about mental health. So, what does this tell us, especially those of us navigating the ? Let's dive in. complex world of marketing and community engagement The Power of Asking "How Are You?" First off, when was the last time a brand genuinely asked you how you were doing? And I mean, really asked, not just as a prelude to selling you something. Elmo's simple question struck a chord because it felt real, unfiltered, and devoid of any marketing agenda. It's a powerful reminder that behind every screen, there's a human being, possibly waiting for a sign that someone cares. But here's a twist: this isn't just about how brands interact with consumers. It's a two-way street. Behind every brand logo, social media post, or promotional email, there's a person. Someone who's navigating their own set of challenges, trying to bridge the gap between corporate objectives and genuine human connection. It's easy to forget that in our rush to express dissatisfaction or demand attention, we're addressing fellow human beings. People who, just like us, appreciate kindness and understanding. This realization brings me an important reflection: kindness should be the cornerstone of our interactions online, not an afterthought. In a world often criticized for its capitalist drives, where transactions often overshadow genuine human interaction, choosing kindness IS revolutionary. Digital spaces need to be better. Kinder. So, the next time a brand asks, "How are you?" consider it an opportunity. An opportunity to engage in a more meaningful, empathetic convo. And when we, as consumers, reach out to brands, let's remember the power of kindness. In our demanding, fast-paced world, a little kindness can go a long way. Let’s create a ripple effect that, despite—or perhaps because of—capitalism, transforms our online interactions into something more human, more connected. Lessons from Pop Culture You know how sometimes a TV show, a song, or even a tweet can just explode and suddenly, everyone's talking about it? Like when the " . The internet couldn't stop gabbing about old times, deep friendships, and how quickly the time flies. Friends" gang got back together Or when Taylor Swift dropped "Folklore" out of the blue, and all of a sudden, we're all deep in feels, talking about escape and the tales we spin in our heads just to get by. And oh, how can we forget the whole "Don't Worry Darling" press tour drama? Talk about a rollercoaster. Or how about the buzz around Lewis Hamilton switching to Ferrari? It's like, for a moment, these stories become the center of our universe, pulling us into a vortex of opinions, emotions, and sometimes, heated debates. These slices of pop culture, including Elmo's heartfelt check-in, really highlight how much sway these moments have over our current discourse. They shine a light on stuff we usually don't talk about, or at least not in the public eye. For brands, these moments underscore the power of storytelling that resonates on a personal level, illustrating the importance of crafting messages that speak directly to the human experience It's fascinating, isn't it? How a simple question, a reunion, a song, or even the hint of scandal can open up floodgates of conversation about deeper, sometimes more complex issues. It's like, through these shared cultural experiences, we find a common ground to explore ideas and feelings that might have stayed buried under the hustle of everyday life. Pretty cool, if you ask me. The Empathy Factor Here’s a question for you: How can brands channel this same level of empathy and engagement without it coming off as insincere or opportunistic? When a tries to be funny, we often shush them off for trying too hard. B2B SaaS LinkedIn page It's a tightrope walk, indeed. The key lies in genuine intention. When Sesame Street followed up with mental health resources, it wasn't just a PR move; it was a responsible, empathetic response to a community's outcry. Imagine if all brands took a similar approach, using their platforms not just to sell but to support, educate, and uplift. It's about adding real value to the lives of the people in your community. Sounds normal yet revolutionary, doesn't it? Being More Than a Brand Ever noticed how some brands just get it? Like Nike, right? They're not just about sneakers and sports gear. They stand up for what's right, , sparking conversations about equality and grit. championing athletes no matter their background And then there's Ben & Jerry's. Sure, their ice cream is to die for, but have you seen how they dive headfirst into the big issues? Climate change, social justice—you name it, they're on it, pushing us all to think a bit harder about the world we're spooning that Cherry Garcia into. ( ) Cherry Garcia is not their best flavor, and I think that’s something we can all agree on These brands, they're playing a bigger game. They're not just logos or catchy jingles. They're out there, making a difference, being a shoulder to lean on, and heck, sometimes a megaphone for change. Consider the journey of a small, local coffee shop that used its platform to highlight community stories during the pandemic, offering a virtual space for shared experiences and support. Or think about a tech startup that provides free resources to educators, demonstrating a commitment to education beyond its product offering. These examples show that no matter your size, your brand can have a meaningful impact by focusing on what matters most—people. And I know what you’re thinking, "That's all cool for them, but what about smaller companies? We're just starting, or maybe we're a mid-sized company trying to build our community. How do we show we're more than just another brand?" First off, start with what you stand for. What's the heart and soul of your brand? Maybe you're all about sustainability, or perhaps community service gets you fired up. Use that. Let it shine through your content, your products, even your customer service. Small acts and consistent messaging can start big conversations. Second, listen and act. It's one thing to say you care, and another to show it. Engage with your community. Hear their stories, their struggles, and their triumphs. Then, see where you can lend a hand, offer a platform, or simply amplify their voices. It's about creating a space where empathy leads the way. Remember, being more than a brand isn't about the size of your marketing budget. You can start right where you are. The Challenge and Opportunity for Brands Some might wonder, 'Can brands truly afford to prioritize empathy in cutthroat markets?' Absolutely. It's not just a nice-to-have; it's a must-have for building lasting relationships. Skeptics may argue that the bottom line is king, but remember, consumers are increasingly choosing brands that stand for something more. Showing genuine empathy isn't just good ethics; it's smart business. Now, back to you. How can your brand or the brands you admire follow in these footsteps? How can you leverage your platform to not only drive sales but also drive positive change? It starts with listening—truly listening—to your community. From there, it’s about taking meaningful action, whether that’s through sharing resources, starting conversations, or showing up for your audience in ways that matter most. Elmo’s check-in was a small gesture with a big impact. It’s a call to action for all of us in the digital space to remember the power of empathy, the importance of community, and the potential we have to make a difference. In the end, it’s not just about what we want to say but about what our audience needs to hear. So, how will your brand respond? Let’s make the internet a little less sad, together.

Elmo Checks In, Everything Is Not Okay: A Lesson in Empathy for Brands

Many UX researchers find themselves immersed in data analysis daily. However, I believe that the essence of our research should not stray from the experiential core – the people involved and the contextual environment. This is what captivates me most about this profession, allowing me to step into diverse communities, understand their thoughts, and contribute to shaping the future they envision. Over the years, friends often inquire about my field research experiences, so I've decided to share two small stories. Story 1 — Transformative Love After Trauma I once participated in a project utilizing AI in home healthcare. Data revealed that over ninety percent of stroke patients abandoned rehabilitation within a month after returning home. The primary reason, as indicated by surveys, was a lack of motivation for rehab at home. Simple exchanges with family members only yielded comments like, "He (the patient) is lazy and unwilling to move, so I have to help him." Is it just laziness? Understanding the true reasons only became clear when I immersed myself in the patient's daily life. I interviewed a stroke survivor, a former professor, whose wife asked me to touch his upper arm. In that moment, I felt the atrophy of his deltoid muscle, almost detached from the bone. It was then that I realized the so-called "laziness" was due to muscles no longer under voluntary control, exacerbated by potential cognitive impairment common in stroke survivors. As I listened to the professor, in his 60s, crying out like a child, I, as an observer, couldn't bring myself to witness such scenes. It was hard to fathom the psychological struggles faced by the patient's family. For family members, the challenge wasn't just caring for their loved ones day and night but fundamentally changing the way they expressed love after their partner fell ill. This is a cruelty most people cannot fathom, let alone achieve. It's this unbearable burden that leads many families, in their eagerness to care, to inadvertently forsake their loved ones before forsaking treatment. This interview was the only one I had to cut short due to overwhelming emotions. In the latter part, I silently accompanied the professor and his wife through muscle strength exercises. As we parted ways, the professor insisted on seeing us off to the roadside to wait for a ride, a habit he maintained, his wife explained, since their early days together. "This habit is the only thing he remembers." His wife smiled faintly, patting his hair. After integrating the insights from this research into the design concept, we developed a smart home rehabilitation device that connects patient health data to hospital networks. This provides real-time professional guidance for those unable to undergo long-term in-hospital rehabilitation, with the first step being to disseminate basic knowledge and avoid missing the optimal recovery window due to overzealous family care. Story 2 — Impacting Men's Mindset: A Means of Caring for Pregnant Women and Children Several years ago, I traveled to Uganda to assist a local telecom operator and NGO in establishing a mobile-based consulting platform to improve the nutrition of pregnant women and children. Each day, accompanied by a local social worker/ translator, I visited various villages to understand the dietary habits and changes during pregnancy. As we shared a local lunch, I interviewed a woman who had recently given birth to her third child. I asked her husband, who earned a living by selling egg pancakes, about the lunch he prepared for his wife. "Same as usual, maize flour porridge (posho). She can't eat too much; the baby will be too big for delivery." "Do you not eat egg pancakes yourselves?" "No, we don't. The eggs are for selling." "What do you feed the other two children?" "They love pineapples, and I bought some for them for 500 shillings (equivalent to the price of two eggs)." "Why spend so much on buying fruit for them? Do you know the benefits of fruit for their health?" "I don't know, it just tastes good. If I earn more, they can have the food they like. When they grow strong and smart, they can lead a good life for the family." "What would you most like to buy for the children to eat?" "Pork." He whispered mysteriously in my ear. The pork was the most expensive food in their eyes. The social worker/ translator waved us off, accusing him of boasting and saying he couldn't afford pork. I realized that this might be the most crucial discovery of the day. Fathers hold the purchasing power for food, own mobile phones, and can access nutritional education information. Rather than focusing on mothers, we need to impact men’s mindset. In the end, these on-the-ground insights were successfully integrated into our research findings. Using the Theory of Reasoned Action model, we analyzed elements such as family members' behavior attitudes and motivations, constructing an opportunity roadmap to change dietary habits for pregnant women and children in rural Uganda. Statistical Data Out of Context is Meaningless I've often encountered skepticism regarding the scientific rigor and credibility of qualitative research. I want both those familiar and unfamiliar with ethnographic research to know that we value insights from real-life situations, ensuring our data stays rooted in the complexity of the real world rather than confined to statistical objectivity. As Nate Silver mentioned in "The Signal and the Noise," "Data is meaningless without context." The key to making effective predictions is not restricting oneself to quantitative information but rather striking a proper balance between qualitative and quantitative information. David Brooks, in "Heroes of Uncertainty," suggests that many fields, including economics, education, and psychiatry, have been distorted by hard scientific standards. He argues that a mixed mindset will be more suitable for these fields, one that occupies a place in both science and the humanities and can bring multiple advantages. Engaging in experience research is no different. As UX researchers, we always advocate the joint interpretation of objectivity and subjectivity, carefully considering the relevance and causality of data and information. As experience research has evolved from focusing solely on interactive experiences to diving deeper into users' implicit motivations, and specific contextual needs, and even considering business strategies, it demands the integration of resources and the use of innovative methods to tackle challenges and opportunities. This is why, at tech companies, the Experience Research team collaborates closely with the Data Science team. For example, intriguing patterns identified in Data Science experimental data can be combined with contextual research for thorough analysis and insights, revealing the underlying causes of observed phenomena. This collaborative approach helps bridge gaps and overcome biases from each perspective, enabling us to formulate precise research questions and attain more profound, confident results. Will the Era of Big Data and Algorithms Devalue "Feelings"? Every day, we find ourselves submerged beneath an overwhelming deluge of information. This makes us increasingly doubtful of the reliability of our feelings and judgments, rendering it easy to surrender trust to algorithms. In his book, "21 Lessons for the 21st Century," Yuval Noah Harari discusses the issue of data hegemony and human fairness, presenting a daunting vision: “As authority shifts from humans to algorithms, we may no longer see the world as the playground of autonomous individuals struggling to make the right choices. Instead, we might perceive the entire universe as a flow of data, see organisms as little more than biochemical algorithms, and believe that humanity’s cosmic vocation is to create an all-encompassing data-processing system – and then merge into it.” This is a future we, as researchers in user experience, do not want to see. Users becoming conditioned data-generating creatures and the datafication of experiences might result in manipulating judgments and choices, rendering human minds and feelings inconsequential. Any platform or product with access to data must respect the values and experiences of users, returning autonomy to them. Data and information should be used to help us create more value for users and better experiences. Cherish the emotions we have, for the future cannot be a Truman Show.

Empathy in Data: Unveiling the Human Stories Behind UX Research

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