Transparent, explainable, and accountable AI for robotics

By Sandra Wachter, Brent Daniel Mittelstadt, Luciano Floridi. 2017

291 citations recorded by the source corpus.

Fair, accountable AI and robotics need precise regulation and better methods to certify, explain, and audit inscrutable systems. To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

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Citations in the corpus

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018, methodology): “This information is then used to assess the appropriateness of the outcome in relation to the concerns and values of users [13, 53, 15, 54].”
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning (2019, background): “d to be interpreted by human, which largely limits their applications in some high-stake scenarios like autonomous cars and medical diagnosis, where explanations are important for scrutable decisions (3). To this end, the concept of interpretable machine learning (IML) has been formally proposed (4)”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, background): “Thus, explainable AI has – for good reason – become a prominent keyword in technology ethics (Mittelstadt et al., 2016; Wachter et al., 2017).”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “Developers and stakeholders need to establish a clear framework for accountability, which may include monitoring the performance of AI models, addressing biases and errors, and responding to the concerns of users and aﬀected communities [100, 101, 102].”
TED: Teaching AI to Explain its Decisions (2018, background): “The demand for explanation has manifested itself in new regulations that call for automated decision making systems to provide “meaningful information” on the logic used to reach conclusions (Goodman and Flaxman 2016; Wachter, Mittelstadt, and Floridi 2017b; Selbst and Powles 2017).”
Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0 (2020, background): “Data has been raised so far mainly regarding the fields of AI and Robotics, discussing its relationship with regulation and innovation [167] without reaching a solution.”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Transparency The capacity of a method to explain how the system works even when it behaves unexpectedly [77, 26, 13, 95, 85, 14, 15, 71, 55, 16, 96, 87] Transferability The capacity of a method for explainability to transfer prior knowledge to unfamiliar situations [56]”
The grand challenges of Science Robotics (2018, background): “Thus, it is problematic that the European Union (EU) General Data Protection Regulation does not include an explicit right to an explanation when decisions affecting people are reached “solely” algorithmically ( 99 ).”
A Review on Ergonomics in Agriculture. Part I: Manual Operations (2020, background): “However, a number of barriers, ranging from social [3] and ethical [93] aspects, to technological [94] and monetary [95] aspects, still prevent a wide assimilation of robotic technologies in agricultural production systems.”
Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery (2019, background): “In this regard, Wachter et al (2017) 35 believe that systems can make unfair and discriminatory decisions, develop bias, or have unexpectedly dangerous behaviour.”
How People Explain Action (and Autonomous Intelligent Systems Should Too) (2017, background): “The call for Autonomous Intelligent Systems (AIS) to be transparent has recently become loud and clear (Wachter, Mittelstadt, and Floridi 2017).”