Government Solicits Submissions From Academic and Research Centers for Responsible AI Development

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The government has put out a call inviting proposals from academic institutions and research and development organizations. These proposals aim to create “tools and frameworks” that advance the ethical development of artificial intelligence. The focus areas encompass machine unlearning, synthetic data generation, fairness tools, bias mitigation strategies, and other pertinent themes.

One of these proposed tools concentrates on machine unlearning, designed to rectify inaccuracies, biases, and outdated information within machine learning models. As stated in the proposal document, “Machine unlearning algorithms assist in removing undesirable learned behaviors, fostering the creation of more accurate, reliable, and fair AI systems across various domains.”

Additionally, the call seeks proposals for tools and frameworks addressing synthetic data generation, algorithm fairness, bias mitigation, ethical AI, and governance testing. The emphasis lies in identifying data gaps, addressing privacy concerns, and advocating for equitable representation in AI.

The eligibility criteria for applying to this initiative are stringent. Government-run institutions like IITs, NITs, IIITs, R&D establishments, and private academic organizations with existing lab infrastructure are eligible. However, they must demonstrate the ability to conclude the project within the next two years while aligning with the overarching objective of promoting fair and ethical AI utilization.

The proposals need to be submitted to the Ministry of Electronics and Information Technology by January 12 to be considered for the initiative.

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Government Solicits Submissions From Academic and Research Centers for Responsible AI Development

The government has put out a call inviting proposals from academic institutions and research and development organizations. These proposals aim to create “tools and frameworks” that advance the ethical development of artificial intelligence. The focus areas encompass machine unlearning, synthetic data generation, fairness tools, bias mitigation strategies, and other pertinent themes.

One of these proposed tools concentrates on machine unlearning, designed to rectify inaccuracies, biases, and outdated information within machine learning models. As stated in the proposal document, “Machine unlearning algorithms assist in removing undesirable learned behaviors, fostering the creation of more accurate, reliable, and fair AI systems across various domains.”

Additionally, the call seeks proposals for tools and frameworks addressing synthetic data generation, algorithm fairness, bias mitigation, ethical AI, and governance testing. The emphasis lies in identifying data gaps, addressing privacy concerns, and advocating for equitable representation in AI.

The eligibility criteria for applying to this initiative are stringent. Government-run institutions like IITs, NITs, IIITs, R&D establishments, and private academic organizations with existing lab infrastructure are eligible. However, they must demonstrate the ability to conclude the project within the next two years while aligning with the overarching objective of promoting fair and ethical AI utilization.

The proposals need to be submitted to the Ministry of Electronics and Information Technology by January 12 to be considered for the initiative.

Disclaimer

We strive to uphold the highest ethical standards in all of our reporting and coverage. We StartupNews.fyi want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.

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