Ethical Artificial Intelligence (AI)

A Ethical Artificial Intelligence (AI) is a artificial intelligence that is designed and implemented in a manner that adheres to established ethical principles and moral values.

• Context:
  • It can (typically) involve considerations related to fairness, transparency, accountability, and privacy.
  • It can (typically) address the need to ensure AI systems respect human rights, promote human dignity, and avoid harm, as highlighted by UNESCO's first-ever global standard on AI ethics, which emphasizes transparency, fairness, and human oversight.
  • It can (often) involve debates around the potential for AI systems to make autonomous ethical decisions, with discussions on creating tests like the Ethical Turing Test to evaluate an AI's ethical decision-making capabilities.
  • It can include efforts to teach AI systems right from wrong, advancing our understanding of human ethics and exploring the use of specific learning algorithms that adhere to social norms of transparency and predictability.
  • It can (often) focus on minimizing bias in AI systems and ensuring they do not perpetuate or exacerbate social inequality or discrimination.
  • It can (often) include the development of AI governance frameworks and ethical guidelines for AI research and deployment.
  • It can be crucial in sectors like healthcare, finance, and law enforcement, where AI decisions can have significant impacts on human lives.
  • It can be a topic of ongoing research, especially in the context of rapidly advancing AI technologies and their integration into various aspects of society.
  • It can involve interdisciplinary collaboration between computer scientists, ethicists, legal experts, and other stakeholders.
  • It can be challenged by the dynamic nature of technology and the need for ethical standards to evolve alongside technological advancements.
  • ...
• Example(s):
  • The development of an AI system in healthcare that prioritizes patient confidentiality and informed consent.
  • AI algorithms in hiring processes designed to reduce gender and racial biases.
  • Bias Monitoring Tools,
  • Ethical AI Framework,
  • The UNESCO Recommendation on the Ethics of Artificial Intelligence, adopted by all 193 Member States, which sets a global standard for ethical AI.
  • The Ethical Turing Test, proposed as a way to evaluate whether an AI's decisions are ethical.
  • ...
• Counter-Example(s):
  • A biased facial recognition system used in law enforcement.
  • AI chatbots that propagate misinformation.
  • AI systems that operate without transparent decision-making processes.
  • AI applications that exacerbate existing inequalities or biases without attempts at mitigation.
  • Black Box Algorithms,
  • Surveillance AI,
  • Autonomous Weapons.
• See: AI Ethics, Responsible AI, AI Regulation, Algorithmic Bias, AI Governance, Data Privacy, Machine Ethics, Roboethics, Transparency in AI.

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References

2024a

• (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Ethical Artificial Intelligence (AI) in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-02-18.
  • QUOTE: Ethical Artificial Intelligence (AI) refers to the design, development, and application of AI technologies in a manner that aligns with widely accepted moral principles, values, and standards of conduct. At its core, ethical AI seeks to ensure that AI systems operate in a transparent, fair, and accountable way, minimizing harm while promoting the wellbeing and dignity of all individuals. This involves addressing challenges related to privacy, security, bias, discrimination, and the potential for job displacement, among other concerns. The goal is to create AI systems that enhance human capabilities without infringing upon human rights or exacerbating social inequalities.

    To implement ethical AI, developers, policymakers, and other stakeholders must engage in ongoing dialogue and collaboration, considering the broad societal, economic, and environmental implications of AI technologies. This includes adopting frameworks and guidelines that incorporate ethical considerations at every stage of the AI lifecycle, from design to deployment and beyond. By doing so, ethical AI aims to harness the transformative potential of AI to address pressing global challenges, improve quality of life, and foster a more equitable and sustainable future, while mitigating the risks that these powerful technologies pose.

2024b

• (UNESCO, 2024) ⇒ https://www.unesco.org/en/artificial-intelligence/recommendation-ethics Retrieved: 2024-02-18.
  • QUOTE: The rapid rise in artificial intelligence (AI) has created many opportunities globally, from facilitating healthcare diagnoses to enabling human connections through social media and creating labour efficiencies through automated tasks.

    However, these rapid changes also raise profound ethical concerns. These arise from the potential AI systems have to embed biases, contribute to climate degradation, threaten human rights and more. Such risks associated with AI have already begun to compound on top of existing inequalities, resulting in further harm to already marginalised groups.

2024c

• (Weir et al., 2024) ⇒ Charles Weir, Anna Dyson, Olamide Jogunola, Louise Dennis, and Katie Paxton-Fear. (2024). “Interlinked Computing in 2040: Safety, Truth, Ownership, and Accountability.” In: Computer, 57(1). doi:10.1109/MC.2023.3318377

2024d

• (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence Retrieved:2024-2-18.
  • The ethics of artificial intelligence is the branch of the ethics of technology specific to artificially intelligent systems. It is sometimes divided into a concern with the moral behavior of humans as they design, make, use and treat artificially intelligent systems, and a concern with the behavior of machines, in machine ethics.

2023

• (Muller, 2023) ⇒ Vincent C. Müller (2023). "Ethics of Artificial Intelligence and Robotics". In:The Stanford Encyclopedia of Philosophy (Fall 2023 Edition), Edward N. Zalta & Uri Nodelman (eds.).
  • QUOTE: Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these.

2021

• (Overby, 2021) ⇒ Stephanie Overby (2021). "What is ethical Artificial Intelligence (AI)? 7 questions, answered". In: The Enterprisers Project.
  • QUOTE: Do you have some anxiety about Artificial Intelligence (AI) bias or related issues? You’re not alone. Nearly all business leaders surveyed for Deloitte’s third State of AI in the Enterprise report expressed concerns around the ethical risks of their AI initiatives.

    There is certainly some cause for uneasiness. Nine out of ten respondents to a late 2020 Capgemini Research Institute survey were aware of at least one instance where an AI system had resulted in ethical issues for their businesses. Nearly two-thirds have experienced the issue of discriminatory bias with AI systems, six out of ten indicated their organizations had attracted legal scrutiny as a result of AI applications, and 22 percent have said they suffered customer backlash because of these decisions reached by AI systems.

2019

• (Jobin et al., 2024) ⇒ Anna Jobin, Marcello Ienca, and Effy Vayena (2019). "The Global Landscape of AI Ethics Guidelines.". In: Nature Machine Intelligence, vol. 1, no. 9, pp. 389-399.
  • QUOTE: In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be ‘ethical’, there is debate about both what constitutes ‘ethical AI’ and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

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