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 trustworthy AI systems. They have responded with great efforts of designing more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.

An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research

“…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.…”

“…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].…”

“…Responding to this challenge, in the recent past there has been a plethora of interest from ML researchers and practitioners to develop algorithms and models that embody qualities such as fairness, explainability, privacy, and robustness. This sub-field of ML is often referred by umbrella terms such as Responsible ML or Trustworthy ML [3,30,32].…”

“…FAccT 1 and AIES 2 ), with participation from fields like social sciences and public policy, such mapping is important to not only summarize existing work but also to inform new application areas on their relevance to TwML. To this end, there are a number of quality review and summary articles [3,5,16], as well as books [11]. However, by nature these are qualitative and static, and leave the judgement of a topic of interest being relevant to TwML to the reader.…”

Responsible AI: Gender bias assessment in emotion recognition

“…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].…”