AfroKwary

🚨 [AI GOVERNANCE] The African Observatory on Responsible AI published the report "Responsible AI Governance in Africa: Prospects for Outcomes Based Regulation," and it's a must-read for everyone interested in global AI governance challenges. Quotes: "At an African level, fewer than 10 countries have adopted a national AI strategy. The common objectives of these AI country strategies include a focus on governance through the establishment of national AI councils, national data strategies, investments in infrastructure, skills and capacity building as well as research and development. Crucially, these strategies offer no insights on what the governance of AI may look like in the future. Further, they offer no clarity around the kind of socio-economic outcomes that the governance must help produce. Consequently, the first pillar in the AU continental strategy is harness human capital development for AI and a strong desire for African governments to collaborate for AI to empower Africa to overcome developmental challenges and drive socio-economic transformation." (page 10) "While AI policies are emerging across Africa and countries such as Egypt, Mauritius and Rwanda have published their national AI strategies, Africa remains dominated by foreign technology and the foreign AI companies are not necessarily supporting the realization of the national developmental priorities such as those outlined in the AU’s Agenda 2063. Outcomes-based regulations which are formulated based on local socio-economic realities compel regard for these realities notwithstanding the prescriptions in corporate policies of these companies." (page 19) "The preferred approach to regulation is changing globally. 73 As countries grapple with the challenge of regulating an industry that is evolving and not fully understood, a spectrum of governance models for AI is emerging. For emerging economies, outcomes-based regulation should be preferred in acknowledgment of the harm that the rapid adoption of unregulated AI-based systems can cause. This paper has highlighted both the benefits and challenges of outcomes-based regulation of AI. Exercising oversight over the use of AI must focus on mitigating the effects of the technology rather than micromanaging the technology itself. This entails an agile approach of setting risk-based performance standards based on consistently current information of the developments and trends in AI." (page 31) ➡ Read the report below. ➡ To stay up to date with the latest developments in AI policy & regulation, join 29,300+ people who subscribe to my weekly newsletter (link below). #AIgovernance #AIpolicy #AIregulation #Africa #GlobalAIGovernance

Panel Title: Realities and Potential on the Africa Continent - Stanford Institute for Human-Centered Artificial Intelligence (HAI) The panel suggests that there is a need for systemic changes to ensure that AI adoption in Africa benefits the continent's people and promotes economic growth, innovation, and development. #Speakers Justin Arenstein | CEO, Code for Africa Barbara Glover | Program Officer – African Union High-Level Panel on Emerging Technologies, African Union Development Agency-NEPAD Vukosi Marivate | UP ABSA Chair of Data Science, University of Pretoria Kiito Shilongo | Senior Tech Policy Fellow, Mozilla Some key points mentioned by the panel include: - The importance of language as a catalyst for AI adoption in Africa - The need for guardrails and policies that ensure responsible AI development and deployment - The challenges of coordination between different countries, organizations, and stakeholders - The potential risks of AI being used to perpetuate social and economic inequalities - The need for normative frameworks and guidelines to promote ethical AI practices - The importance of human rights in the development and application of AI https://lnkd.in/eHH3Wsft #AI #africa #afrokwary

2024 IJCAI International Joint Conferences on Artificial Intelligence Organization - CALL FOR PAPERS (DEADLINE - April 26, 2024) Trustworthy AI aims to provide an explainable, robust, and fair decision-making process. In addition, transparency and security also play a significant role in improving the adoption and impact of ML solutions. Currently, most ML models assume ideal conditions and rely on the assumption that test/clinical data comes from the same distribution of the training samples. The inconsistency between the data and model with the population at hand also poses a lack of transparency and explainability in the decision-making process. 🔔 Call for Papers Submissions that address the following and related topic areas: -Transparency, explainability, and interpretability in ML models. -Security and privacy concerns in ML models. -Misinformation detection. Data poisoning and adversarial examples. -Audit techniques for data and ML models. -Fairness and exclusion studies (benchmarks and datasets). -Evaluation for ensuring fair outcomes for all, especially underrepresented groups. -Robustness, safety, and collective value alignment, with a specific interest in measures that support developing countries and underrepresented communities. -Social good, participatory AI, and applications of the above principles to different domains (e.g. healthcare, loans, legal). 🔗 Apply here: https://lnkd.in/eFjU57ZJ Organizing Committee Members: John Johnwambura Aisha Alaagib Siobhan Mackenzie Hall Celia Cintas #Afrokwary #ResponsibleAI #AIEthics #AISafety #IJCAI