Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation

Khan, Masood; Vice, Jordan

doi:10.36227/techrxiv.19102094

Cited by 2 publications

(1 citation statement)

References 46 publications

(61 reference statements)

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“…The study proposes measuring the impact of public perception on trust in AI, focusing on two dimensions: control of AI and ethics in AI. These dimensions, along with a mediating factor called mood, are crucial for building trust in AI technologies (Khan, 2022). Arrieta et al (2020) delve into the realm of Explainable Artificial Intelligence (XAI), offering a comprehensive overview of its concepts, taxonomies, and the opportunities and challenges it presents for responsible AI.…”

Section: Role Of Public Perception and Trust In Ai Adoptionmentioning

confidence: 99%

Ethical AI in practice: Balancing technological advancements with human values

Benjamin Samson Ayinla,

Olukunle Oladipupo Amoo,

Akoh Atadoga

et al. 2024

Int. J. Sci. Res. Arch.

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In an era where artificial intelligence (AI) increasingly intersects with every facet of human life, the imperative for ethical AI has never been more pronounced. This paper delves into the complex interplay between technological advancements in AI and the overarching human values that guide societal norms. The background of the study establishes the urgency of addressing ethical challenges inherent in AI, such as privacy, bias, and accountability, within the broader context of regulatory and policy frameworks. Aiming to critically evaluate the integration and effectiveness of ethical principles in AI applications, the paper navigates through a qualitative analysis, employing theoretical frameworks to dissect the ethical dimensions of AI. The scope encompasses a diverse range of topics, including global trends in ethical AI development, the impact of AI on human rights and personal freedoms, and the analysis of bias and fairness in AI algorithms. Real-world case studies provide insights into the successes and failures of ethical AI implementation, while the role of public perception and trust in AI adoption is scrutinized. The main conclusions reveal a dynamic global landscape of ethical AI, emphasizing the need for robust ethical frameworks and proactive strategies to mitigate biases and ensure equitable outcomes. Recommendations advocate for clear ethical guidelines, integration of ethics in AI development, transparency, accountability, multi-stakeholder collaboration, public engagement, and continuous ethical evaluation. The study concludes that balancing technological innovation with ethical constraints is crucial for the responsible development of AI. It underscores the importance of ethical vigilance, ensuring AI aligns with societal values and individual rights.

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Section: Role Of Public Perception and Trust In Ai Adoptionmentioning

confidence: 99%

Ethical AI in practice: Balancing technological advancements with human values

Benjamin Samson Ayinla,

Olukunle Oladipupo Amoo,

Akoh Atadoga

et al. 2024

Int. J. Sci. Res. Arch.

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A Taxonomy of Explanation Types and Need Indicators in Human–Agent Collaborations

Wachowiak,

Coles,

Canal

et al. 2024

Int J of Soc Robotics

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In recent years, explanations have become a pressing matter in AI research. This development was caused by the increased use of black-box models and a realization of the importance of trustworthy AI. In particular, explanations are necessary for human–agent interactions to ensure that the user can trust the agent and that collaborations are effective. Human–agent interactions are complex social scenarios involving a user, an autonomous agent, and an environment or task with its own distinct properties. Thus, such interactions require a wide variety of explanations, which are not covered by the methods of a single AI discipline, such as computer vision or natural language processing. In this paper, we map out what types of explanations are important for human–agent interactions, surveying the field via a scoping review. In addition to the typical introspective explanation tackled by explainability researchers, we look at assistive explanations, aiming to support the user with their task. Secondly, we survey what causes the need for an explanation in the first place. We identify a variety of human–agent interaction-specific causes and categorize them by whether they are centered on the agent’s behavior, the user’s mental state, or an external entity. Our overview aims to guide robotics practitioners in designing agents with more comprehensive explanation-related capacities, considering different explanation types and the concrete times when explanations should be given.

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Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation

Cited by 2 publications

References 46 publications

Ethical AI in practice: Balancing technological advancements with human values

Ethical AI in practice: Balancing technological advancements with human values

A Taxonomy of Explanation Types and Need Indicators in Human–Agent Collaborations

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