IGF 2022 Lightning Talk #29 Using trustworthy AI to create a better world

Time
Wednesday, 30th November, 2022 (13:40 UTC) - Wednesday, 30th November, 2022 (14:10 UTC)
Room
Speaker's Corner

KPMG NL
Auke Pals, Consultant KPMG NL, Private Sector, WEOG

Speakers

Auke Pals, Consultant KPMG NL, Private Sector, WEOG

Onsite Moderator

Auke Pals

Online Moderator

TBD

Rapporteur

TBD

SDGs

17. Partnerships for the Goals

Targets: Organisations are engaged in digital transformation. Algorithms and other advanced analytical solutions are on the basis on many impactful decisions. Having a trustworthy algorithms should be therefore on the basis of creating these decisions. Many decisions are doing so, by creating trustworthy algorithms SDGs are tackled. The above mentioned SDGs will be addressed in the presentation.

Format

The lightning talk will have a presentation format, with room for questions and discussion.

Duration (minutes)
30
Language

English

Description

Many organisations are engaged in digital transformation, with algorithms and other advanced analytical solutions increasingly being part of operational management. For anyone it is very important to be able to rely on the insights, decisions and actions performed by these solutions in order to take full advantage of technologies such as Artificial Intelligence (AI).

The data and analysis collected and processed are an essential foundation for addressing SDG challenges. As the pace of the technology advances, the volumes of data continues to grow exponentially. While challenges exist in governing the technologies and causing impactful decisions.

One example of these challenges is in health. This industry is under financial stress, increasing complexity of disease and co-morbidity, and burdened by capacity constraints -- why has data not been healthcare's savvier? Three major challenges have inhibited this:

1. data is not accessible and remains in silos;

2. data is not analysed to derive meaningful (clinical) insights;

3. insight isn't accessible for actioning by providers or patients to self/joint manage their condition.

In this talk we'll dive deeper into how to liberate, analyse, and action that data in a trustworthy way. 

We will dive deeper in how this is applied in the EPI - Enabling Personalized Interventions project which will develop a platform based upon a secure and trustworthy distributed data infrastructure, combining data analytics, including machine learning, and health decision support algorithms to create new, actionable, and personalized insights for prevention, management, and intervention to providers and patients. 

Format: This lightning talk will focus on how data trust challenges can be tackled.

 

Key Takeaways (* deadline 2 hours after session)

AI Governance is the basis of Trustworthy AI

Federated Learning is an extremely good option to safeguard trustworthy dataflows

Call to Action (* deadline 2 hours after session)

Support more research of trust by design

Implement pro-actively AI Governance in your organization

Session Report (* deadline 26 October) - click on the ? symbol for instructions

Many organisations are engaged in digital transformation, with algorithms and other advanced analytical solutions increasingly being part of operational management. For anyone it is very important to be able to rely on the insights, decisions and actions performed by these solutions in order to take full advantage of technologies such as Artificial Intelligence (AI).

The data and analysis collected and processed are an essential foundation for addressing SDG challenges. As the pace of the technology advances, the volumes of data continues to grow exponentially. While challenges exist in governing the technologies and causing impactful decisions.

One example of these challenges is in health. This industry is under financial stress, increasing complexity of disease and co-morbidity, and burdened by capacity constraints -- why has data not been healthcare's savvier? Three major challenges have inhibited this:

1. data is not accessible and remains in silos;

2. data is not analysed to derive meaningful (clinical) insights;

3. insight isn't accessible for actioning by providers or patients to self/joint manage their condition.

We showed how this is applied in the EPI - Enabling Personalized Interventions project which will develop a platform based upon a secure and trustworthy distributed data infrastructure, combining data analytics, including machine learning, and health decision support algorithms to create new, actionable, and personalized insights for prevention, management, and intervention to providers and patients.