On July 23rd, Trinnovo Group hosted its latest Zurich panel discussion with the help of industry-leading voices in deep technology and regulated services.
From algorithmic trading to personalised drug development, pioneers everywhere strive to balance innovation and compliance in a new era of regulation. We explored the most pressing challenges facing the AI-enabled world today, including ethics, the complexities of regulation, tech’s impact on the future of work and more.
Thank you to our incredible audience and speakers for heading down to Technoparkstrasse for an evening to remember. We were joined by:
Anthony Kelly – Host, Chair and Co-Founder of DeepRec.ai, Trinnovo Group
Ana-Maria Manda – Head of AI - Adoption Services Centre, SAP
Matthias Greiller – Independent Senior Advisor, AML
Samata Bhutra – Head of Generative AI Lounge & Services, Zurich Insurance
Christoph Schwarz– Chief Technology Officer, Radicant Bank
It’s events like this that help build a much-needed sense of community in the tech and regulatory spaces, and we’re grateful for the opportunity to meet like-minded professionals from all over the world. Discover our talking points below, but note that our events operate under Chatham House rules, meaning the information disclosed can be shared, but the source cannot be identified.
Complex Compliance
From localised cultural awareness to compliance across borders, the AI uptick is a global phenomenon, therefore we must think globally. AI is still in its infancy, and the world’s regulatory bodies have been slow to keep up with the rate of its evolution.
AI-focused transformation projects cannot be seen as a one-time investment. As the regulatory landscape grows ever more complex, a continuous commitment to investment, upskilling, and creating a security-minded culture is the best forward.
Reliability
Systems are evolving at warp speed. Given the rate of technological advancements, they can look unrecognisable in six months, partly thanks to the need for businesses to stay competitive and win market share.
On the flip side, this makes it difficult to both regulate and ensure the reliability of said systems – what do we need to focus on to make a tangible impact? Transparency is a large piece of the puzzle and one of the most difficult to get right. When you can’t trust where the data is coming from, adoption will be slow.
Explainable AI is essential in an era of growing privacy concerns, security-first cyber functions, and tech-savvy consumers.
To avoid unexplainable AI, humans will need to stay in the loop at every touchpoint of the development and implementation process – the safety of the user depends on it.
Data
From the build phase through to enterprise-level tech adoption, the AI journey always starts with data. Data quality is paramount in the development of high-quality, unbiased AI systems. The robustness of data sets defines the fairness of AI, a key enabler of accuracy, privacy, and compliance.
Generally speaking, whenever we interact with AI, we’re training it. Firms (and indeed, individual users) should be asking themselves ‘What are they doing with my data?’ Data is a powerful tool, and everyone has a part to play in using it to drive this advanced technology in the right direction.
Security and Safeguarding
Quality assurance frameworks built on key principles must be put in place early on. We’ve seen the rise of deep fakes come to an apex in the last few years (take the recent $25 million security breach in Hong Kong for example), a reality that cybersecurity experts warned the world about over a decade ago. In a digital-first world, cybersecurity awareness is more important than ever.
More regulation is on the way, which, in many ways, has been spearheaded by the transposition of the EU AI Act. As organisations adapt to these new regulations, a proactive approach to company-wide learning is essential.
Culture and AI
Culture and AI are inextricable. If data and security are at the core of ethical AI, embedding and adopting it is a matter of culture. You need the best people to enable technological implementation, underpinned by diversity and inclusion.
If you lack the inclusivity to field and retain diverse teams, it will negatively affect the quality of your AI products and hinder your ability to adopt new technologies.
The moment your testing teams have access to a diverse range of experiences, perspectives, and backgrounds, is the moment your products and technologies can begin to appeal to a wider range of people.
Whether that’s in the way users interact with your products or the nature of the challenges your systems solve, diverse thinking alone can serve the needs of a diverse humanity.
While the human element is the cornerstone of ethics in AI, approaching the very concept of ethics is not an easy process, particularly given its global-facing nature – for example, how we define ethics in Switzerland could be very different to the US, presenting a myriad of challenges at the first step of the journey. The 10 UNESCO principles for ethics in AI serve as a human-centred approach:
1. Proportionality and Do No Harm
2. Safety and Security
3. Right to Privacy and Data Protection
4. Multi-stakeholder and Adaptive Governance and Collaboration
5. Responsibility and Accountability
6. Transparency and Explainability
7. Human Oversight and Determination
8. Sustainability
9. Awareness and Literacy
10. Fairness and Non-Discrimination
Sustainability
AI will impact our sustainability efforts in some unlikely ways, and it’s keeping regulators busy trying to predict potential risks. Leaders will need to share the same goals as the regulators to successfully.
As energy consumption rises and E-Commerce picks up, there is an opportunity to develop cheaper models through optimised power distribution.
Firms have a chance to forecast energy consumption with greater accuracy, enabling decision-makers to better adapt to their climate goals in the fight for a more sustainable future.
Talk to Our Teams
Hoping to hire world-class talent on time and in budget? Talk to our recruitment consultants to find out more about our market-leading talent solutions:
Anthony Kelly, DeepRec.ai: Computer Vision, NLP, Data Science, Machine Learning, C++, and Blockchain.
Viki Dowthwaite, Trust in SODA: Cloud & Infrastructure, Software Engineering, Data, DevOps, and Creative.
