Explainability in Autonomous Pedagogical Agents

Tulli, Silvia

doi:10.1609/aaai.v34i10.7141

Cited by 1 publication

(1 citation statement)

References 8 publications

(8 reference statements)

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“…In order to trust ML in education, ML models need to make sure stakeholders (i.e., parents, teachers, and administration) understand the decisionmaking processes [196], [197]. There are already some efforts in this directions [198], [199].…”

Section: A Explainable ML For Smart City Applicationsmentioning

confidence: 99%

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

Ahmad¹,

Maabreh²,

Ghaly³

et al. 2020

Preprint

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As we make tremendous advances in machine learning and artificial intelligence (ML/AI) technosciences, there is a renewed understanding in the ML/AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book "Smart Enough City", the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of ML/AI algorithms that will form the technological bedrock of future cities. A number of research institutes on humancentered AI (HAI) have been established at top international universities-including Stanford Institute for Human-Centered Artificial Intelligence (HAI), Berkeley's Center for Human-Compatible AI (CHAI), and MIT Institute for Data, Systems, and Society (IDSS). Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human-Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI/ML in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.

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Section: A Explainable ML For Smart City Applicationsmentioning

confidence: 99%