Recognizing Operators’ Duties to Properly Select and Supervise AI Agents – A (Better?) Tool for Algorithmic Accountability

In November of 2020, the Privacy Commissioner of Canada proposed creating GDPR-inspired rights for decision subjects and allowing financial penalties for violations of those rights. Shortly afterward, the proposal to create a right to an explanation for algorithmic decisions was incorporated into Bill C-11, the Digital Charter Implementation Act. This commentary proposes that creating duties for operators to properly select and supervise artificial agents would be a complementary, and potentially more effective, accountability mechanism than creating a right to an explanation. These duties would be a natural extension of employers’ duties to properly select and retain human employees. Allowing victims to recover under theories of negligent hiring or supervision of AI-system-as-agents would reflect their increasing (but less than full) autonomy and avoid some of the challenges that victims face in proving the foreseeability elements of other liability theories

The Relationships of Personality and Cognitive Styles with Self-Reported Symptoms of Depression and Anxiety

Many studies have reported concurrent relationships between depressive symptoms and various personality, cognitive, and personality-cognitive vulnerabilities, but the degree of overlap among these vulnerabilities is unclear. Moreover, whereas most investigations of these vulnerabilities have focused on depression, their possible relationships with anxiety have not been adequately examined. The present study included 550 high school juniors and examined the cross-sectional relationships among neuroticism, negative inferential style, dysfunctional attitudes, sociotropy, and autonomy, with a wide range of anxiety and depressive symptoms, as well as the incremental validity of these different putative vulnerabilities when examined simultaneously. Correlational analyses revealed that all five vulnerabilities were significantly related to symptoms of both anxiety and depression. Whereas neuroticism accounted for significant unique variance in all symptom outcomes, individual cognitive and personality-cognitive vulnerabilities accounted for small and only sometimes statistically significant variance across outcomes. Importantly, however, for most outcomes the majority of symptom variance was accounted for by shared aspects of the vulnerabilities rather than unique aspects. Implications of these results for understanding cognitive and personality-cognitive vulnerabilities to depression and anxiety are discussed

Recognizing Operators’ Duties to Properly Select and Supervise AI Agents – A (Better?) Tool for Algorithmic Accountability

In November of 2020, the Privacy Commissioner of Canada proposed creating GDPR-inspired rights for decision subjects and allowing financial penalties for violations of those rights. Shortly afterward, the proposal to create a right to an explanation for algorithmic decisions was incorporated into Bill C-11, the Digital Charter Implementation Act. This commentary proposes that creating duties for operators to properly select and supervise artificial agents would be a complementary, and potentially more effective, accountability mechanism than creating a right to an explanation. These duties would be a natural extension of employers’ duties to properly select and retain human employees. Allowing victims to recover under theories of negligent hiring or supervision of AI-system-as-agents would reflect their increasing (but less than full) autonomy and avoid some of the challenges that victims face in proving the foreseeability elements of other liability theories

Framework for Promoting Workforce Well-being in the AI-Integrated Workplace

The Partnership on AI’s “Framework for Promoting Workforce Well-being in the AI- Integrated Workplace” provides a conceptual framework and a set of tools to guide employers, workers, and other stakeholders towards promoting workforce well-being throughout the process of introducing AI into the workplace. As AI technologies become increasingly prevalent in the workplace, our goal is to place workforce wellbeing at the center of this technological change and resulting metamorphosis in work, well-being, and society, and provide a starting point to discuss and create pragmatic solutions. The importance of making a commitment to worker well-being in earnest has been highlighted by the COVID-19 public health and economic crises which exposed and exacerbated the long-standing inequities in the treatment of workers. Making sure those are not perpetuated further with the introduction of AI systems into the workplace requires deliberate efforts and will not happen automatically. The Framework is designed to initiate and inform discussions on the impact of AI that strengthen the reciprocal obligations between workers and employers, specifically focusing on worker well-being.1 The four tools that make up the Framework are both interrelated and independent. Use of these tools will differ by implementing organization and will depend on multiple considerations, such as funding, ability to dedicate time, stage of AI integration, etc. This paper draws upon existing work by academics, labor unions, and other institutions to explain why organizations should prioritize worker well-being. In doing so, it explores the need for a coherent AI and workforce well-being framework. It also attempts to account for different forms of AI integration into the workplace, outlines the different instances in which workers may encounter AI, and the technological aspects of AI that may impact workers. Relevant literature has been synthesized into Six Pillars of Workforce Well-being that should be prioritized and protected throughout AI integration. Human rights is the first pillar, and supports all aspects of workforce well-being. The five additional pillars of well-being include physical, financial, intellectual, emotional well-being, as well as purpose and meaning

A Multilevel Perspective on Self-Determination Theory: Predictors and Correlates of Autonomous and Controlled Motivation

Based on Self-Determination Theory (SDT), we examined mediational models connecting autonomy support and self-criticism to negative affect [NA], positive affect [PA], and goal progress [GP] via autonomous and controlled motivation. Separate measures were obtained within eight domains (e.g., academic performance and intimate relationships) for 346 university students. Multilevel structural equation modeling was used to test whether, both between-persons and within-person, autonomy support and self-criticism predicted autonomous and controlled motivation, which in turn predicted NA, PA, and GP. In addition to several between-persons indirect effects, we found numerous significant within-person indirect effects, including: 1) in domains where they experienced greater autonomy support, people experienced greater PA and greater GP, mediated by greater autonomous motivation and 2) in domains where they experienced greater self-criticism, people experienced more NA mediated by greater controlled motivation, and less PA mediated by greater controlled motivation and lesser autonomous motivation. These results support systematically adopting a multilevel perspective in SDT research

The effects of self-criticism and self-oriented perfectionism on goal pursuit

Five separate studies examined the associations of self-criticism and self-oriented perfectionism with goal pursuit across a variety of domains. Although self-criticism has previously been shown to be related to diminished goal progress, a controversy remains regarding the potential association between aspects of "positive perfectionism," such as self-oriented perfectionism, and enhanced goal progress. The results of the five studies demonstrated a consistent pattern of negative association between self-criticism and goal progress. The results also showed a positive association between self-oriented perfectionism and goal progress when self-criticism was controlled. The important role of self-criticism for understanding the impact of perfectionistic concerns is highlighted by these results. Implications for the debate concerning the possible positive effects of perfectionistic strivings are also discussed

Ability to receive compassion from others buffers the depressogenic effect of self-criticism: A cross-cultural multi-study analysis

Self-criticism has been shown to be a vulnerability factor that can lead to and maintain depression. We examined the moderating effect of fear of receiving compassion from others on the positive association between self-criticism and depression. Self-report measures were administered to four separate samples (total N = 701) varying in age (students and community adults) and cultural context (Canada, England, and Portugal). Two different measures of self-criticism and of depression were administered to investigate the generalizability of results. Self-criticism, depression, and fear of compassion from others were positively related to one another in all samples. As predicted, fear of compassion from others exerted a moderating effect on the relationship between self-criticism and depression. Low fear of compassion from others weakened the depressogenic effect of self-criticism, while high fear of compassion from others exacerbated the effect. Thus, a self-critic's ability to be open and responsive to care and support from others protected against depression. The aggregate moderating effect across the four studies was of medium size (d + = .53) and highly significant, indicating a robust phenomenon. Implications for working with self-critical depressed patients are discussed.N/