How one organization are tackling algorithmic bias and what you can do today
Join Co-founder Riham Satti, and Research Manager Dr Luke Jew for a discussion about algorithmic bias, the impact this has on decision making, and subsequent implications for organisations. In this session we will share the key approaches MeVitae have taken in developing ethical HR tools, and the practical steps you can take to ensure you are using AI ethically.
Our brains are not well adapted to decision making in the modern world, and to overcome these limitations, we increasingly rely on automated algorithms to help us, including the HR world. However, these algorithms are also imperfect and can be dogged by algorithmic biases. After recognizing the diversity and inclusion challenges organizations faced, and the impact algorithmic and cognitive bias has on this, the MeVitae team spent years researching and collaborating with Oxford University and the European Space Agency, understanding how Artificial Intelligence and cognitive neuroscience could be leveraged to tackle these issues. Dubbed “Augmented Intelligence”, MeVitae developed technology that harnesses the power of human cognition and artificial intelligence to help organizations hire smarter, faster, and fairer.
Agenda:
• What is algorithmic bias and why is it important today?
• What are the causes of algorithmic biases?
• What are the impacts of algorithmic biases in the HR world?
• What are the different framework areas you should consider when mitigating AI biases, from data minimization to statistical accuracy
• Tips and techniques for mitigating algorithmic bias
Key Session Outcomes:
• To understand what algorithmic biases are.
• To understand how algorithmic biases limit our judgment, and the implications this has on our hiring decisions.
• To consider the organizational problems that occur with a lack of diversity.
• To explore current solutions to mitigate biases, including MeVitae’s blind recruiting.
• To understand how Augmented Intelligence (IA) can be used to tackle AI biases
