AI governance is a legal term in artificial intelligence (AI) that covers a range of management facets, including ethical and technical issues.
In order to minimize dangers and optimize the advantages of AI systems, a framework of rules, moral principles, standards, and procedures must be established. This is known as AI governance. AI governance is a topic of discussion that addresses issues such as explainability, responsibility for AI decisions, equity of outcomes (bias mitigation) and broad societal effects of AI.
In general, AI governance establishes who oversees the operation of AI and how much daily life algorithms can influence.
Why is AI governance important?
When machine learning algorithms are utilized for decision-making, AI governance becomes imperative. Biases in machine learning, especially when it comes to racial profiling, might lead to the inaccurate identification of basic user data. In addition to deceiving law enforcement in identifying criminal suspects, this may lead to discriminatory denial of credit and healthcare to individuals. The optimal course of action in situations when AI-based judgments may be unfair or violate human rights is determined by AI governance.
The necessity for strong governance to minimize harm and uphold public trust has been brought to light by high-profile errors like the Tay chatbot, in which a Microsoft AI chatbot learned toxic behavior via public interactions on social media, and the COMPAS software's biased sentencing decisions.
AI's potential for harm has grown as it becomes more and more integrated into corporate and governmental processes. According to an IBM IDC whitepaper, achieving a condition of compliance, trust, and efficiency in the creation and use of AI technology depends on AI governance. Furthermore, the White House Office of Science and Technology Policy has elevated AI governance and policy to a national priority in the United States, even going so far as to solicit public opinion on the risks and advantages of AI.
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