An executive level survey of the key issues and implications of ethics and the responsible use of data. There is a heavy focus on how to examine, detect, and mitigate bias in data and algorithm outcomes
Analytics and artificial Intelligence (AI) is rapidly growing and can create professional and reputational risks along with bias. The new Ethical & Responsible Use of Data & Predictive Model Executive Track Certificate Program from the Society of Actuaries (SOA) is designed to educate busy professionals managing teams responsible for providing data and AI insights to regulators, governmental agencies, consulting companies, and ensuring the results are not skewed with any potential bias.
The program provides an in-depth understanding of the risks and implications of algorithmic bias and unfair discrimination with an emphasis on what questions managers need to ask their team to mitigate risk.
The Society of Actuaries’ Ethical & Responsible Use of Data & Predictive Models Executive Track is a 30-hour virtual course consisting of six e-Learning modules offering the most comprehensive and streamlined training available. It is ideal for C-Suite Executives, Mid-to-Senior-Level Managers, or first-time Mangers who oversee teams responsible for accurate data and AI without bias.
This streamlined program will provide in-depth training to ensure managers are grounded in best practices which include:
- Helping to develop or establish a common data and analytics ethical and responsible use program.
- Offering a framework with ethical criteria to consider when working with predictive models and algorithms.
- Helping to develop and ensure adherence to a common framework for actuarial and data teams to ensure they are speaking the same language and operating under the same umbrella of best practices.
- Providing tools to assess whether actuarial and data teams have a deep understanding of ethical issues and how market and regulatory context affects models.
- Understanding approaches to mitigate risks associated with the many ethical questions the industry is facing with big data on the frontier of actuarial science.