Thread by @Saad_Alrayes

1/11: The use of #AI & machine learning (#ML) in financial services brings benefits, but also novel challenges & amplified risks. The question of whether AI can be managed through existing regulations or requires a new approach is crucial in ensuring the interests of the markets.

2/11: Achieving regulatory excellence in #AI regulation involves clear & transparent regulations, a risk-based approach, stakeholder engagement, science-based decision-making, regulatory cooperation, continuous improvement, flexibility, efficient processes & accountability.

3/11: The heterogeneity & constant evolution of #AI algorithms require multiple specialised regulatory agencies to effectively regulate their use. These agencies must build data science capacity, employ flexible strategies & remain vigilant in their regulation.

4/11: Regulators should consider utilising ML tools themselves to enhance their ability to protect the public from the adverse effects of AI. Effective regulatory governance of AI is possible with the constant pursuit of regulatory excellence. #MachineLearning #AIGovernance

5/11: Achieving regulatory excellence involves collaboration between regulatory agencies, industry stakeholders & the public. It requires the adoption of strategies & continuous refinement of processes to protect the public interest while promoting innovation & economic growth.

6/11: There is a polarised view on regulation, with some advocating for heavy regulation to address risks, while others criticise strict & costly regulations for hindering economic recovery. The performance & impact of regulation remain subjects of debate.

7/11: Regulatory Breakdown calls for systematic analysis & questioning of conventional wisdom about #regulation, regardless of political stance. Understanding the causes of regulatory crises is crucial for adopting effective regulation policies.

8/11: Achieving regulatory excellence is challenging due to the balancing act required to meet multiple objectives & consider the interests of various stakeholders. The complex & shifting nature of the regulation necessitates humility, discretion & restraint in regulatory actions

9/11: Regulators should be guided by scientific evidence while recognising its limits in decision-making. Scientific evidence provides reasons for regulations, but the regulatory process also involves questions of values & ethics. A balanced approach is necessary.

10/11: Regulators play a significant role in imposing costs on regulated entities. A systematic assessment of the costs & benefits of regulatory interventions is crucial. Responsiveness to opportunities is key to achieving regulatory excellence.

11/11 My recommendations lean toward: ‘Regulating #AI & ML requires humility, discretion & regulatory restraint. The complexity & rapid evolution of these techs call for a collaborative approach. Let's approach it with care, fostering innovation by evidence-based decision-making’