Artificial empathy

Artificial empathy (AE) or computational empathy is the development of AI systems − such as companion robots or virtual agents − that are able to detect and respond to human emotions in an empathic way.[1] According to scientists, although the technology can be perceived as scary or threatening by many people,[2] it could also have a significant advantage over humans in professions which are traditionally involved in emotional role-playing such as the health care sector.[3] From the care-giver perspective for instance, performing emotional labor above and beyond the requirements of paid labor often results in chronic stress or burnout, and the development of a feeling of being desensitized to patients. However, it is argued that the emotional role-playing between the care-receiver and a robot can actually have a more positive outcome in terms of creating the conditions of less fear and concern for one's own predicament best exemplified by the phrase: "if it is just a robot taking care of me it cannot be that critical." Scholars debate the possible outcome of such technology using two different perspectives. Either, the AE could help the socialization of care-givers, or serve as role model for emotional detachment.[3][4]

A broader definition of artificial empathy is "the ability of nonhuman models to predict a person's internal state (e.g., cognitive, affective, physical) given the signals (s)he emits (e.g., facial expression, voice, gesture) or to predict a person's reaction (including, but not limited to internal states) when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music, etc.)". [5]

Areas of research
Main articles: Philosophy of artificial intelligence and Ethics of artificial intelligence

There are a variety of philosophical, theoretical, and applicative questions related to AE. For example:

  1. Which conditions would have to be met for a robot to respond competently to a human emotion?
  2. What models of empathy can or should be applied to Social and Assistive Robotics?
  3. Does the interaction of humans with robots have to imitate affective interaction between humans?
  4. Can a robot help science learn about affective development of humans?
  5. Would robots create unforeseen categories of inauthentic relations?
  6. What relations with robots can be considered truly authentic?

Examples of AE research and practice

Humans often communicate and make decisions based on the inferences of other’s internal states (e.g., emotional, cognitive and physical states) from the various signals emitted by the person, such as facial expression, body gesture, voice and words. Broadly speaking, the domain of AE focuses on developing non-human models to achieve similar objectives using the data emitted by or shown to humans.

Streams of AE research

The concept of AE has been applied in various research disciplines, including artificial intelligence and business. Specifically, there have been two main streams of research in this domain: first, the use of nonhuman models in predicting a person's internal state (e.g., cognitive, affective, physical) given the signals he or she emits (e.g., facial expression, voice, gesture); second, the use of nonhuman models in predicting a person's reaction when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music etc.).[5]

Research on affective computing, such as emotional speech recognition and facial expression detection, falls within the first stream of AE. Contexts that have been studied include oral interviews,[6] call center[7] human-computer interaction,[8] sales pitch,[9] and financial reporting.[10] The second stream of AE have been researched more in marketing contexts, such as advertising,[11] branding,[12][13] customer reviews,[14] in-store recommendation system,[15] movies,[16] and online dating.[17]

AE applications in practice

With the increasing volume of visual, audio and text data in commerce, there have been many business applications using AE. For example, Affectiva[18] analyses viewers' facial expressions from video recordings while they are watching video advertisements in order to optimize the content design of video ads. HireVue,[19] a hiring intelligence firm, help firms make recruitment decisions using analysis of the audio and video information from candidates' video interviews. Lapetus Solutions[20] develops a model to estimate an individual's longevity, health status and disease susceptibility from a face photo. Their technology has been applied in the insurance industry.[21]

Artificial empathy and human services

Although AI has not been shown to replace social workers themselves yet, the technology has begun making waves in the field. Social Work Today published an article in 2017 describing research performed at Florida State University. The research involved the use of computer algorithms to analyze health records and detect combinations of risk factors that could indicate a future suicide attempt. The article reports, "machine learning—a future frontier for artificial intelligence—can predict with 80% to 90% accuracy whether someone will attempt suicide as far off as two years into the future. The algorithms become even more accurate as a person's suicide attempt gets closer. For example, the accuracy climbs to 92% one week before a suicide attempt when artificial intelligence focuses on general hospital patients".

At this point in time, artificial intelligence has not been able to replace social workers completely, but algorithmic machines such as those described above can have incredible benefits to social workers. Social work operates on a cycle of engagement, assessment, intervention, and evaluation with clients. This technology can make the assessment for risk of suicide can lead to earlier interventions and prevention, therefore saving lives. It is the hope of these researchers that the technology will be implemented in our modern healthcare system. The system would learn, analyze, and detect risk factors, alerting the clinician of a patient’s suicide risk score (equivalent to a patient’s cardiovascular risk score). At this point, social workers could step in for further assessment and preventative intervention.

gollark: Ah, it appears that I am *incapable* of reading.
gollark: Or possibly coltrans/olivia, i.e. the generally competent people.
gollark: I suspect this is quintopia.
gollark: +**5**?!
gollark: Wow, impressive.

See also

References
  1. Yalçın, Ö.N., DiPaola, S. Modeling empathy: building a link between affective and cognitive processes. Artificial Intelligence Review 53, 2983–3006 (2020). doi:10.1007/s10462-019-09753-0.
  2. Jan-Philipp Stein; Peter Ohler (2017). "Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting". Cognition. 160: 43–50. doi:10.1016/j.cognition.2016.12.010. ISSN 0010-0277. PMID 28043026.
  3. Bert Baumgaertner; Astrid Weiss (26 February 2014). "Do Emotions Matter in the Ethics of Human-Robot Interaction?" (PDF). Artificial Empathy and Companion Robots. European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 288146 (“HOBBIT”); and the Austrian Science Foundation (FWF) under grant agreement T623-N23 (“V4HRC”) – via direct download.
  4. Minoru Asada (14 February 2014). "Affective Developmental Robotics" (PDF). How Can We Design the Development of Artifcial Empathy?. Osaka, Japan: Dept. of Adaptive Machine Systems, Graduate School of Engineering, Osaka University – via direct download.
  5. Xiao, L., Kim, H. J., & Ding, M. (2013). "An introduction to audio and visual research and applications in marketing". Review of Marketing Research, 10, p. 244. doi:10.1108/S1548-6435(2013)0000010012.
  6. Hansen, J. H., Kim, W., Rahurkar, M., Ruzanski, E., & Meyerhoff, J. (2011). "Robust emotional stressed speech detection using weighted frequency subbands". EURASIP Journal on Advances in Signal Processing, 2011, 1–10.
  7. Lee, C. M., & Narayanan, S. S. (2005). "Toward detecting emotions in spoken dialogs. IEEE transactions on speech and audio processing, 13(2), 293–303.
  8. Batliner, A., Hacker, C., Steidl, S., Nöth, E., D'Arcy, S., Russell, M. J., & Wong, M. (2004, April). "'You Stupid Tin Box'-Children Interacting with the AIBO Robot: A Cross-linguistic Emotional Speech Corpus". In Lrec.
  9. Allmon, D. E., & Grant, J. (1990). Real estate sales agents and the code of ethics: A voice stress analysis. Journal of Business Ethics, 9(10), 807–812.
  10. Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2012). Analyzing speech to detect financial misreporting. Journal of Accounting Research, 50(2), 349–392.
  11. Xiao, L., & Ding, M. (2014). "Just the faces: Exploring the effects of facial features in print advertising". Marketing Science, 33(3), 338–352.
  12. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
  13. Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research, 51(4), 463–479.
  14. Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science, 35(6), 953–975.
  15. Lu, S., Xiao, L., & Ding, M. (2016). A video-based automated recommender (VAR) system for garments. Marketing Science, 35(3), 484-510.
  16. Liu, X., Shi, S. W., Teixeira, T., & Wedel, M. (2018). Video content marketing: The making of clips. Journal of Marketing, 82(4), 86–101.
  17. Zhou, Yinghui, Shasha Lu, & Min Ding (2020), "Contour-as-Face (CaF) Framework: A Method to Preserve Privacy and Perception", Journal of Marketing Research, forthcoming.
  18. "Home".
  19. "Pre-employment Testing & Video Interviewing Platform".
  20. "Lapetus Solutions, Inc".
  21. "CHRONOS - Get Started".
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