responsible AI

Extracted Attributes

• Core Principles

  Fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability

• Associated Risks

  Algorithmic bias, technological unemployment, AI-enabled misinformation, and existential risks

• Governance Tools

  Responsible AI Standard, Responsible AI Scorecard, and Responsible AI Dashboard

• Corporate Oversight

  Office of Responsible AI and C-level leadership/Board advisory groups

• Primary Application Areas

  Healthcare, education, criminal justice, and the military

Timeline

• Google pauses Gemini's image generation of people following controversy over historical inaccuracies and bias attributed to internal responsible AI influences. (Source: Document dc653a59-1711-437d-95cf-b5b07878217e)

  2024-02-22

• Google and Reddit announce a $60 million per year licensing deal for AI training data. (Source: Document dc653a59-1711-437d-95cf-b5b07878217e)

  2024-02-22

• Klarna announces its AI assistant, built with OpenAI technology, is performing the work equivalent to 700 full-time customer service agents. (Source: Document dc653a59-1711-437d-95cf-b5b07878217e)

  2024-02-27

• Apple cancels its electric car initiative, Project Titan, to shift resources and focus toward artificial intelligence development. (Source: Document dc653a59-1711-437d-95cf-b5b07878217e)

  2024-02-29

Ethics of artificial intelligence

The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes algorithmic biases, fairness, accountability, transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks. Some application areas may also have particularly important ethical implications, like healthcare, education, criminal justice, or the military.

Web Search Results

• What is Responsible AI - Azure Machine Learning

  Responsible Artificial Intelligence (Responsible AI) is an approach to developing, assessing, and deploying AI systems safely, ethically, and with trust. AI systems result from many decisions made by their creators. Responsible AI helps guide these decisions—from defining system purpose to user interaction—toward more beneficial and equitable outcomes. It keeps people and their goals at the center of design and respects values like fairness, reliability, and transparency. [...] Microsoft created a Responsible AI Standard, a framework for building AI systems based on six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles are the foundation of a responsible and trustworthy approach to AI, especially as intelligent technology becomes more common in everyday products and services. [...] In addition, the Responsible AI scorecard in Azure Machine Learning creates accountability by enabling cross-stakeholder communication. The scorecard empowers developers to configure, download, and share model health insights with both technical and non-technical stakeholders. Sharing these insights helps build trust. Azure Machine Learning also supports decision-making by informing business decisions through:

• Responsible AI: Ethical policies and practices

  Back to Making responsible AI attainable section;) ## Frequently asked questions Responsible AI is a set of steps we take to make sure that AI systems are trustworthy and uphold societal principles. It involves working through issues such as fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability. And thinking deeply about the ways that we design, build, and operate AI systems. [...] Microsoft's responsible AI principles include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles guide the development and deployment of AI systems to ensure they treat everyone equally and prevent discrimination based on personal characteristics. Microsoft also emphasizes the importance of validating AI models responsibly to enhance fairness and alignment with reality. [...] Microsoft’s AI principles To introduce AI responsibly, organizations should develop a Responsible AI Standard, like Microsoft's, covering principles such as fairness, reliability, privacy, and inclusiveness. Here are additional steps organizations may take to ensure responsible AI use: + Establish an Office of Responsible AI to oversee ethics and governance. + Implement AI governance tools like the Microsoft Responsible AI Dashboard to monitor and manage AI systems.

• What is responsible AI?

  Responsible artificial intelligence (AI) is a set of principles that help guide the design, development, deployment and use of AI—building trust in AI solutions that have the potential to empower organizations and their stakeholders. Responsible AI involves the consideration of a broader societal impact of AI systems and the measures required to align these technologies with stakeholder values, legal standards and ethical principles. Responsible AI aims to embed such ethical principles into AI [...] This applies particularly to the new types of generative AI that are now being rapidly adopted by enterprises. Responsible AI principles can help adopters harness the full potential of these tools, while minimizing unwanted outcomes.

• Responsible AI - Generative AI Lens

  As with any new technology, generative AI creates new challenges as well. Potential users must evaluate the promise of the technology while also analyzing the risks. Responsible AI is the practice of designing, developing, and using AI technology with the goal of maximizing benefits and minimizing risks. At AWS, we define responsible AI using a core set of dimensions that we assess and update over time as AI technology evolves: [...] The opportunity is clear: by implementing responsible AI practices from day one, you position your organization to lead in the AI-enabled future, building solutions that drive innovation while maintaining the trust that is fundamental to long-term success. ## Moving forward [...] Elements of the Responsible AI framework are weighted more heavily for generative AI systems as opposed to traditional machine learning solutions (like veracity or truthfulness). However, the implementation of Responsible AI requires a systematic review of the system along the defined dimensions. ## Responsible AI: Aligning innovation with your mission

• Responsible AI

  Skip to content # Responsible AI Definition: Responsible AI refers to the practice of designing and managing AI systems that are trustworthy, explainable, and human-centric. ## Overview [...] Artificial Intelligence (AI) offers many advantages for companies but comes with significant risks. Companies using AI or creating AI systems must adopt specific rules, methods, and technologies to reduce these risks. Responsible AI means ensuring AI does not hurt people, the companies that use it, or the environment. [...] Responsible AI usually starts as a top priority initiative for senior executives and the board of directors. The initial leader of a company’s Responsible AI initiative is often a C-level leader. It is also common for the company’s board of directors to create a special advisory group to guide the AI strategy and ensure AI is used responsibly.