A framework for ethical artificial intelligence - from social theories to cybernetics-based implementation

2021 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Kushal Anjaria
Keyword(s):  
Artificial Intelligence ◽  
Social Theories

scholarly journals The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study (Preprint)

2021 ◽  
Author(s):  
Claire Woodcock ◽  
Brent Mittelstadt ◽  
Dan Busbridge ◽  
Grant Blank
Keyword(s):  
Artificial Intelligence ◽  
Temporal Arteritis ◽  
Statistical Significance ◽  
Group Differences ◽  
P System ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Information Gap ◽  
Social Theories ◽  
The Impact

BACKGROUND Artificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including <i>why</i>-explanations and <i>how</i>-explanations. Social theories suggest that <i>why</i>-explanations are better at communicating knowledge and cultivating trust among laypeople. OBJECTIVE The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease. METHODS A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants’ responses followed by comparison-of-means tests were used to evaluate group differences in trust. RESULTS Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (<i>P</i>=.65) and temporal arteritis, marginally significant (<i>P</i>=.09). Varying disease by explanation type resulted in statistical significance for input influence (<i>P</i>=.001), social proof (<i>P</i>=.049), and no explanation (<i>P</i>=.006), with counterfactual explanation (<i>P</i>=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user’s specific question and discount the diseases that they may also be aware of. CONCLUSIONS System builders developing explanations for symptom-checking apps should consider the recipient’s knowledge of a disease and tailor explanations to each user’s specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.

scholarly journals The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

10.2196/29386 ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. e29386
Author(s):  
Claire Woodcock ◽  
Brent Mittelstadt ◽  
Dan Busbridge ◽  
Grant Blank
Keyword(s):  
Artificial Intelligence ◽  
Temporal Arteritis ◽  
Statistical Significance ◽  
Group Differences ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Information Gap ◽  
Social Theories ◽  
Social Proof ◽  
The Impact

Background Artificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including why-explanations and how-explanations. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople. Objective The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease. Methods A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants’ responses followed by comparison-of-means tests were used to evaluate group differences in trust. Results Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (P=.65) and temporal arteritis, marginally significant (P=.09). Varying disease by explanation type resulted in statistical significance for input influence (P=.001), social proof (P=.049), and no explanation (P=.006), with counterfactual explanation (P=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user’s specific question and discount the diseases that they may also be aware of. Conclusions System builders developing explanations for symptom-checking apps should consider the recipient’s knowledge of a disease and tailor explanations to each user’s specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.

scholarly journals AI and social theory

AI & Society ◽  
2021 ◽  
Author(s):  
Jakob Mökander ◽  
Ralph Schroeder
Keyword(s):  
Artificial Intelligence ◽  
Social Theory ◽  
Predictive Power ◽  
Digital Data ◽  
Wide Range ◽  
Social Theories ◽  
The World ◽  
The Future ◽  
State Breakdown ◽  
Breakdown Model

AbstractIn this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence
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