Ethics, Bias & Algorithmic Fairness in the world of Artificial Intelligence

As we move towards an AI revolution, conversations around Bias, algorithmic fairness and our social responsibility towards AI, as data scientists, has become essential.
Join us at an upcoming startup ETIQ AI to address the problems and discuss potential solutions with some amazing women working in this field!

ETIQ AI

Etiq AI is a tech startup, backed by Zinc VC, which is developing a solution to diagnose and minimise bias and discrimination in machine learning models throughout the AI application lifecycle.

Skewed data is one of the common causes for biased AI models. As machine learning models increasingly make automated decisions about a wide variety of issues such as policing, insurance and what information people see online, the implications of their recommendations become broader. This tool helps encourage the teams who build these models to think of, find and mitigate the negative biases these models amplify at the earliest possible stage before time and money is wasted on biased training methods

The agenda for the meetup will be as follows:

Networking from 6.30 and talks follow from 7 pm.

Speaker: Raluca Crisan, ETIQ AI

Title: Algorithmic Fairness at Etiq AI

Description:

Raluca has 10 years of experience in data science working for a variety of clients (UK retailers, banks & telco companies), and she has been developing software to identify and help mitigate bias in algorithms (bias against certain groups based on eg gender, ethnicity, age, and other characteristics).

Topic:

Raluca will give a brief talk on algorithmic bias, focusing on the fintech/insuretech sectors. As companies in these sectors start using richer data sources, and deeper algorithms, they can run into unintentional bias. She will go over some examples of how bias can insinuate itself in algorithms with the advent of new data sources, as well as a few different ways to identify it and mitigate it that we use at Etiq AI.

Speaker : Catalina Butnaru

Description:
Catalina Butnaru is a City AI and Women in AI Ambassador in London, where she works with the local community of AI practitioners, researchers, academics, and professionals on democratizing access to accurate information about the state of AI and its applications. She was part of the IEEE's Ethics in Action working groups drafting ethical standards for AI and Autonomous systems, and developing measures of transparency, wellbeing, and privacy in AI.

Topic:
Catalina will talk about how to get past resistance to building trustworthy tech and making it our responsibility to think through highly consequential decisions when building AI-enabled products.

Speaker: Ines Marusic, QuantumBlack

Title: "Algorithmic Fairness: From Theory to Practice"

Description:

Ines is a Senior Data Scientist at QuantumBlack , an advanced analytics firm which helps many of the world's largest companies adopt machine learning & leverage big data at scale. She has developed data science products & solutions in a range of different industries including finance, insurance, pharma & elite sport. Ines coordinates the Fairness & Ethics initiative for QuantumBlack's data science practice globally.

Topic:

The advances in machine learning in the past few years have enabled us to automate decisions and processes across many specific tasks. Machine learning is increasingly being used to make decisions that can severely affect people's lives, for instance, in education, hiring, lending & criminal risk assessment. Unfortunately, the data used to train these models often contains undesirable bias that exists in our society.

Ines will discuss the recent advances coming from the machine learning research community on algorithmic fairness. She will also provide a practitioner's perspective by presenting an overview of QuantumBlack's process of incorporating techniques from algorithmic fairness effectively on client data.

meetup image from: https://www.kuppingercole.com/blog/small/the-ethics-of-artificial-intelligence