To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch is launching a series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.
Heidy Khlaaf is an engineering director at the cybersecurity firm Trail of Bits. She specializes in evaluating software and AI implementations within “safety critical” systems, like nuclear power plants and autonomous vehicles.
Khlaaf received her computer science Ph.D. from the University College London and her BS in computer science and philosophy from Florida State University. She’s led safety and security audits, provided consultations and reviews of assurance cases and contributed to the creation of standards and guidelines for safety- and security -related applications and their development.
Q&A
Briefly, how did you get your start in AI? What attracted you to the field?
I was drawn to robotics at a very young age, and started programming at the age of 15 as I was fascinated with the prospects of using robotics and AI (as they’re inexplicably linked) to automate workloads where they’re most needed. Like in manufacturing, I saw robotics being used to help the elderly — and automate dangerous manual labour in our society. I did however receive my Ph.D. in a different sub-field of computer science, because I believe that having a strong theoretical foundation in computer science allows you to make educated and scientific decisions into where AI may or may not be suitable, and where pitfalls may be.
What work are you most proud of (in the AI field)?
Using my strong expertise and background in safety engineering and safety-critical systems to provide context and criticism where needed on the new field of AI “safety.” Although the field of AI safety has attempted to adapt and cite well-established safety and security techniques, various terminology has been misconstrued in its use and meaning. There is a lack of consistent or intentional definitions that do compromise the integrity of the safety techniques the AI community is currently using. I’m particularly proud of “Toward Comprehensive Risk Assessments and Assurance of AI-Based Systems” and “A Hazard Analysis Framework for Code Synthesis Large Language Models” where I deconstruct false narratives about safety and AI evaluations, and provide concrete steps on bridging the safety gap within AI.
How do you navigate the challenges of the male-dominated tech industry, and, by extension, the male-dominated AI industry?
Acknowledgment of how little the status quo has changed is not something we discuss often, but I believe is actually important for myself and other technical women to understand our position within the industry and hold a realistic view on the changes required. Retention rates and the ratio of women holding leadership positions has remained largely the same since I joined the field, and that’s over a decade ago. And as TechCrunch has aptly pointed out, despite tremendous breakthroughs and contributions by women within AI, we remain sidelined from conversations that we ourselves have defined. Recognizing this lack of progress helped me understand that building a strong personal community is much more valuable as a source of support rather than relying on DEI initiatives that unfortunately have not moved the needle, given that bias and skepticism towards technical women is still quite pervasive in tech.
What advice would you give to women seeking to enter the AI field?
Not to appeal to authority and to find a line of work that you truly believe in, even if it contradicts popular narratives. Given the power AI labs hold politically and economically at the moment, there is an instinct to take anything AI “thought leaders” state as fact, when it is often the case that many AI claims are marketing speak that overstate the abilities of AI to benefit a bottom line. Yet, I see significant hesitancy, especially among junior women in the field, to vocalise skepticism against claims made by their male peers that cannot be substantiated. Imposter syndrome has a strong hold on women within tech, and leads many to doubt their own scientific integrity. But it is more important than ever to challenge claims that exaggerate the capabilities of AI, especially those that are not falsifiable under the scientific method.
What are some of the most pressing issues facing AI as it evolves?
Regardless of the advancements we’ll observe in AI, they will never be the singular solution, technologically or socially, to our issues. Currently there is a trend to shoehorn AI into every possible system, regardless of its effectiveness (or lack thereof) across numerous domains. AI should augment human capabilities rather than replace them, and we are witnessing a complete disregard of AI’s pitfalls and failure modes that are leading to real tangible harm. Just recently, an AI system ShotSpotter recently led to an officer firing at a child.
What are some issues AI users should be aware of?
How truly unreliable AI is. AI algorithms are notoriously flawed with high error rates observed across applications that require precision, accuracy and safety-criticality. The way AI systems are trained embed human bias and discrimination within their outputs that become “de facto” and automated. And this is because the nature of AI systems is to provide outcomes based on statistical and probabilistic inferences and correlations from historical data, and not any type of reasoning, factual evidence or “causation.”
What is the best way to responsibly build AI?
To ensure that AI is developed in a way that protects people’s rights and safety through constructing verifiable claims and hold AI developers accountable to them. These claims should also be scoped to a regulatory, safety, ethical or technical application and must not be falsifiable. Otherwise, there is a significant lack of scientific integrity to appropriately evaluate these systems. Independent regulators should also be assessing AI systems against these claims as currently required for many products and systems in other industries — for example, those evaluated by the FDA. AI systems should not be exempt from standard auditing processes that are well-established to ensure public and consumer protection.
How can investors better push for responsible AI?
Investors should engage with and fund organisations that are seeking to establish and advance auditing practices for AI. Most funding is currently invested in AI labs themselves, with the belief that their safety teams are sufficient for the advancement of AI evaluations. However, independent auditors and regulators are key to public trust. Independence allows the public to trust in the accuracy and integrity of assessments and the integrity of regulatory outcomes.