2021
DOI: 10.48550/arxiv.2101.02032
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Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

Abstract: In the current era, people and society have grown increasingly reliant on Artificial Intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, health care, services, and so on. Technologists and AI researchers have a responsibility to develop trus… Show more

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Cited by 9 publications
(15 citation statements)
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References 201 publications
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“…All papers in Table 3 with high relevance scores are on comparatively complex algorithms. A number of these areas have been heavily researched of late, such as reinforcement learning (RL; papers 1,14,19), bandit problems (2,4,18), anomaly detection (2,9,10,11), representation learning (11,13,15), multitask problems (2,8,10,13), dirichlet process (3,22), and nonconvex optimization (24,25). The word-level breakdown of relevance scores (Figure 3) gives further insights into how the concepts in these papers may be related to TwML.…”
Section: Discussionmentioning
confidence: 99%
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“…All papers in Table 3 with high relevance scores are on comparatively complex algorithms. A number of these areas have been heavily researched of late, such as reinforcement learning (RL; papers 1,14,19), bandit problems (2,4,18), anomaly detection (2,9,10,11), representation learning (11,13,15), multitask problems (2,8,10,13), dirichlet process (3,22), and nonconvex optimization (24,25). The word-level breakdown of relevance scores (Figure 3) gives further insights into how the concepts in these papers may be related to TwML.…”
Section: Discussionmentioning
confidence: 99%
“…As indicated by Table 3, Figure 3, and Table 5, potential areas of future exploration include multiclass problems in differential privacy, and work that focus on fairness and transparency aspects of newer research areas in broader ML. Contextually similar non-TwML words in Table 4 suggest the need for more practice-oriented work in this field, which recent studies have acknowledged [3,17].…”
Section: Discussionmentioning
confidence: 99%
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“…Direct discrimination [94] assumes biased results which are depended on particular attributes. Hence, usage of these attributes are considered as protected and defended by law [95,96,97].…”
Section: Related Workmentioning
confidence: 99%