To offer AI-focused women academics and others their well-deserved — and overdue — time within the highlight, TechCrunch is launching a series of interviews specializing in remarkable women who’ve contributed to the AI revolution.
Sarah Bitamazire is the chief policy officer on the boutique advisory firm Lumiera, where she also helps write the newsletter Lumiera Loop, which focuses on AI literacy and responsible AI adoption.
Before this, she was working as a policy adviser in Sweden, focused on gender equality, foreign affairs laws, and security and defense policies.
Briefly, how did you get your start in AI? What attracted you to the sphere?
AI found me! AI has been having an increasingly large impact in sectors that I even have been deeply involved in. Understanding the worth of AI and its challenges became imperative for me to give you the chance to supply sound advice to high-level decision-makers.
First, inside defense and security where AI is utilized in research and development and in lively warfare. Second, in arts and culture, creators were amongst the groups to first see the added value of AI, in addition to the challenges. They helped bring to light the copyright issues which have come to the surface, resembling the continuing case where several every day newspapers are suing OpenAI.
You understand that something is having a large impact when leaders with very different backgrounds and pain points are increasingly asking their advisors, “Are you able to transient me on this? Everyone seems to be talking about it.”
What work are you most pleased with within the AI field?
We recently worked with a client that had tried and did not integrate AI into their research and development work streams. Lumiera arrange an AI integration strategy with a roadmap tailored to their specific needs and challenges. The mixture of a curated AI project portfolio, a structured change management process, and leadership that recognized the worth of multidisciplinary pondering made this project an enormous success.
How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?
By being very clear on the why. I’m actively engaged within the AI industry because there’s a deeper purpose and an issue to unravel. Lumiera’s mission is to offer comprehensive guidance to leaders allowing them to make responsible decisions with confidence in a technological era. This sense of purpose stays the identical no matter which space we move in. Male-dominated or not, the AI industry is large and increasingly complex. Nobody can see the total picture, and we want more perspectives so we will learn from one another. The challenges that exist are huge, and all of us have to collaborate.
What advice would you give to women in search of to enter the AI field?
Entering into AI is like learning a brand new language, or learning a brand new skill set. It has immense potential to unravel challenges in various sectors. What problem do you would like to solve? Learn how AI could be a solution, after which give attention to solving that problem. Carry on learning, and get in contact with folks that encourage you.
What are among the most pressing issues facing AI because it evolves?
The rapid speed at which AI is evolving is a problem in itself. I imagine asking this query often and commonly is a very important a part of having the ability to navigate the AI space with integrity. We do that every week at Lumiera in our newsletter.
Listed below are a number of which are top of mind straight away:
- AI hardware and geopolitics: Public sector investment in AI hardware (GPUs) will more than likely increase as governments worldwide deepen their AI knowledge and begin making strategic and geopolitical moves. Up to now, there’s movement from countries just like the U.K., Japan, UAE, and Saudi Arabia. This can be a space to observe.
- AI benchmarks: As we proceed to rely more on AI, it is crucial to know how we measure and compare its performance. Selecting the precise model for a given use case requires careful consideration. The most effective model on your needs may not necessarily be the one at the highest of a leaderboard. Since the models are changing so fast, the accuracy of the benchmarks will fluctuate as well.
- Balance automation with human oversight: Imagine it or not, over-automation is a thing. Decisions require human judgment, intuition, and contextual understanding. This can’t be replicated through automation.
- Data quality and governance: Where is the nice data?! Data flows in, throughout, and out of organizations every second. If that data is poorly governed, your organization won’t profit from AI, point blank. And in the long term, this might be detrimental. Your data strategy is your AI strategy. Data system architecture, management, and ownership have to be a part of the conversation.
What are some issues AI users should concentrate on?
- Algorithms and data should not perfect: As a user, it will be significant to be critical and never blindly trust the output, especially in case you are using technology straight off the shelf. The technology and tools on top are latest and evolving, so keep this in mind and add common sense.
- Energy consumption: The computational requirements of coaching large AI models combined with the energy needs of operating and cooling the required hardware infrastructure results in high electricity consumption. Gartner has made predictions that by 2030, AI could devour as much as 3.5% of the world’s electricity.
- Educate yourself, and use different sources: AI literacy is vital! To give you the chance to make good use of AI in your life and at work, it’s good to give you the chance to make informed decisions regarding its use. AI should show you how to in your decision-making, not make the choice for you.
- Perspective density: You should involve individuals who know their problem space rather well to be able to understand what kind of solutions that will be created with AI, and to do that throughout the AI development life cycle.
- The identical thing goes for ethics: It’s not something that will be added “on top” of an AI product once it has already been built — ethical considerations must be injected early on and throughout the constructing process, starting within the research phase. This is completed by conducting social and ethical impact assessments, mitigating biases, and promoting accountability and transparency.
When constructing AI, recognizing the constraints of the abilities inside a corporation is crucial. Gaps are growth opportunities: They allow you to prioritize areas where it’s good to seek external expertise and develop robust accountability mechanisms. Aspects including current skill sets, team capability, and available monetary resources should all be evaluated. These aspects, amongst others, will influence your AI roadmap.
How can investors higher push for responsible AI?
Initially, as an investor, you would like to ensure that your investment is solid and lasts over time. Investing in responsible AI simply safeguards financial returns and mitigates risks related to, e.g., trust, regulation, and privacy-related concerns.
Investors can push for responsible AI by taking a look at indicators of responsible AI leadership and use. A transparent AI strategy, dedicated responsible AI resources, published responsible AI policies, strong governance practices, and integration of human reinforcement feedback are aspects to think about. These indicators ought to be a part of a sound due diligence process. More science, less subjective decision-making. Divesting from unethical AI practices is one other option to encourage responsible AI solutions.
