AI Systems and Respect for Human Autonomy

Laitinen, Arto; Sahlgren, Otto

doi:10.3389/frai.2021.705164

Cited by 42 publications

(12 citation statements)

References 42 publications

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“…AI assistants of this type are currently underrepresented on the market ( West et al, 2019 ). However, since both manipulated design factors are frequently discussed in the public discourse on AI ( André et al, 2018 ; West et al, 2019 ; Laitinen and Sahlgren, 2021 ) and could be adjusted in many real-world applications, we believe our experiment to be relevant not only for academia but also for practice.…”

Section: Discussionmentioning

confidence: 99%

Do Men Have No Need for “Feminist” Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs

2022

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Artificial Intelligence (AI) is supposed to perform tasks autonomously, make competent decisions, and interact socially with people. From a psychological perspective, AI can thus be expected to impact users’ three Basic Psychological Needs (BPNs), namely (i) autonomy, (ii) competence, and (iii) relatedness to others. While research highlights the fulfillment of these needs as central to human motivation and well-being, their role in the acceptance of AI applications has hitherto received little consideration. Addressing this research gap, our study examined the influence of BPN Satisfaction on Intention to Use (ITU) an AI assistant for personal banking. In a 2×2 factorial online experiment, 282 participants (154 males, 126 females, two non-binary participants) watched a video of an AI finance coach with a female or male synthetic voice that exhibited either high or low agency (i.e., capacity for self-control). In combination, these factors resulted either in AI assistants conforming to traditional gender stereotypes (e.g., low-agency female) or in non-conforming conditions (e.g., high-agency female). Although the experimental manipulations had no significant influence on participants’ relatedness and competence satisfaction, a strong effect on autonomy satisfaction was found. As further analyses revealed, this effect was attributable only to male participants, who felt their autonomy need significantly more satisfied by the low-agency female assistant, consistent with stereotypical images of women, than by the high-agency female assistant. A significant indirect effects model showed that the greater autonomy satisfaction that men, unlike women, experienced from the low-agency female assistant led to higher ITU. The findings are discussed in terms of their practical relevance and the risk of reproducing traditional gender stereotypes through technology design.

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Section: Discussionmentioning

confidence: 99%

Do Men Have No Need for “Feminist” Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs

2022

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“…Second, optimization for flourishing must be approached delicately, as some principles of optimization at first might seem intrinsically antagonistic to flourishing. For example, autonomy and overcoming challenges is often included as an important facet of flourishing [1,8,57], while naive optimization for some conception of well-being might lead to paternalism and manipulation [58,59], i.e. an ML system that paternalistically takes actions that statistically seem likely to improve an agent's well-being, but are either misinformed or undermine the agent's competence and autonomy.…”

Section: Optimization Pressure For Engagement Undermines Flourishingmentioning

confidence: 99%

Machine Love

Lehman¹

2023

Preprint

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From social media to recommendation and search engines, the impact of machine learning (ML) upon our lives and society continues to grow. While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its impoverished models of us, ML currently falls far short of its exciting potential, which is for it to help us to reach ours. While there is no consensus on defining human flourishing, from diverse perspectives across psychology, philosophy, and spiritual traditions, love is understood to be one of its primary catalysts. Motivated by this view, this paper explores whether there is a useful conception of love fitting for machines to embody. While such machine love might seem like an error of category, historically it has been generative to explore whether some kernel of a nebulous concept, such as life or intelligence, can be thoughtfully abstracted and reimagined in a different medium, as in the fields of machine intelligence or artificial life. This paper forwards a candidate conception of machine love, inspired in particular by work in positive psychology and psychotherapy: to provide unconditional support enabling humans to autonomously pursue their own growth and development. While many alternate conceptions are possible, this one benefits from not requiring machines to simulate emotional affect or relationships, instead focusing on biasing an ML system's actions to support our growth. Through proof of concept experiments, this paper aims to highlight the need for richer models of human flourishing in ML, provide an example framework through which positive psychology can be combined with ML to realize a rough conception of machine love, and demonstrate that current language models begin to enable embodying qualitative humanistic principles. The conclusion is that though at present ML may often serve to addict, distract, or divide us, an alternative path may be opening up: We may align ML to support our growth, through it helping us to align ourselves towards our highest aspirations. * Much of this work was completed during a residency at Stochastic Labs.Preprint. Under review.

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“…Deferring decision-making to ML creates a seismic shift in how users make choices [124]. It, therefore, changes individuals' capacity for self-determination [125,126]. Increasing reliance on algorithmic systems raises questions of whether users' choices are authentic and whether this tendency will impoverish our capacity for self-determination [124,126].…”

Section: Privacy and Autonomymentioning

confidence: 99%

Algorithmic decision-making in financial services: economic and normative outcomes in consumer credit

Sargeant

2022

AI Ethics

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Consider how much data is created and used based on our online behaviours and choices. Converging foundational technologies now enable analytics of the vast data required for machine learning. As a result, businesses now use algorithmic technologies to inform their processes, pricing and decisions. This article examines the implications of algorithmic decision-making in consumer credit markets from economic and normative perspectives. This article fills a gap in the literature to explore a multi-disciplinary approach to framing economic and normative issues for algorithmic decision-making in the private sector. This article identifies optimal and suboptimal outcomes in the relationships between companies and consumers. The economic approach of this article demonstrates that more data allows for more information which may result in better contracting outcomes. However, it also identifies potential risks of inaccuracy, bias and discrimination, and ‘gaming’ of algorithmic systems for personal benefit. Then, this article argues that these economic costs have normative implications. Connecting economic outcomes to a normative analysis contextualises the challenges in designing and regulating ML fairly. In particular, it identifies the normative implications of the process, as much as the outcome, concerning trust, privacy and autonomy and potential bias and discrimination in ML systems. Credit scoring, as a case study, elucidates the issues relating to private companies. Legal norms tend to mirror economic theory. Therefore, this article frames the critical economic and normative issues required for further regulatory work.

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AI Systems and Respect for Human Autonomy

Cited by 42 publications

References 42 publications

Do Men Have No Need for “Feminist” Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs

Do Men Have No Need for “Feminist” Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs

Machine Love

Algorithmic decision-making in financial services: economic and normative outcomes in consumer credit

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