Sycophants, sandbaggers, and schemes: Can we really trust AI in healthcare?: AIMed25

“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”

– Alan Turing, progenitor of AI

One of the questions I received at a recent talk on innovations in the era of AI for medical students and faculty was “How much do we (should we) trust AI?”.

This is especially timely as we have increasingly sophisticated AI models that can purportedly “reason” and “think”. Overall, AI is most likely not acting with intentional malice or deceit, although some would argue that we are not certain about this assumption. AI is, however, sometimes caught between two forces: the initial training and configuration of the model and the subsequent instructions (prompts) these models are given.

The famous AI “paper clip” problem is to ask AI to produce as many paper clips as possible, but not at the expense of acquiring whatever resources it needs to accomplish that objective. Of course we, especially those of us who are old enough, all remember the comical (but insightful) broom scene from Walt Disney’s Sorcerer’s Apprentice. In healthcare, for example, if one would like to deploy AI to achieve the objective of having less people with cancer, we would of course prefer AI to find ways to treat cancer, not have AI allow people with cancer to die without treatment so we have less people with cancer.

With the evolution of the AI models at a fast pace, and now including purported capabilities like thinking and reasoning (but more like stochastic omniscience or reasoning parroting), it is essential that trust between humans and AI is discussed and debated frequently. The following terms (in alphabetical order) with accompanying issues that are good to appreciate in this evolving relationship between humans and AI as regards to trust:

Alignment

This is the process of assuring that there is user-AI agreement of objectives for AI safety. This topic is the title of an excellent book by Brian Christian titled The Alignment Problem. More specifically, AI alignment is the domain that studies how AI systems behave in ways that are beneficial, ethical, and aligned with human values and intensions especially in complex and/or high stakes environments. There are two types of alignment: outer alignment is when the AI system’s objective function reflects what humans want it to do and inner alignment is the AI system’s internal behavior that optimizes that objective. Overall, alignment is assessed at various stages: model design and training, alignment through feedback (post training), interpretability and monitoring, guardrails and controls, and deployment governance (human in the loop).

Chain of thought (CoT)

When the steps of an executed prompt is made transparent, it is called chain of thought or chain of reasoning. This is when AI generates a step-by-step explanation of its process. CoT can improve trust between humans and AI by rendering the AI decision making more transparent, interpretable, and verifiable especially in complex situations like healthcare. Clinicians in particular like to verify, challenge, or refute AI’s output if they can see the entire reasoning process in individual steps, and even provide feedback about certain steps in the chain.

Hallucination vs confabulation

Hallucination is a false sensory perception and arises from a dysfunction in sensory processing. In AI, hallucination is generation of an output that is factually incorrect or fabricated. Confabulation is a fabricated or distorted memory without the intention to deceive and this phenomenon arises from dysfunction in memory systems. Even though confabulation is used interchangeably with hallucination in some instances, confabulation is more based on incomplete memory or training gaps and perhaps more forgiving as a term compared to hallucination. AI’s intention to the complete task that it was asked to execute will do whatever it can to do so, including providing an output that is incorrect. I am not a fan of either of these two terms as the connotation is relatively negative and reflects human behavior that is not favorable. Perhaps these words are examples of anthropomorphizing AI to some degree.

Interpretability vs explainability

Interpretability: How well humans understand the internal mechanics of a model.
Explainability: How well humans can understand and justify a model’s outputs.

For example, a cardiologist may interpret a pacemaker’s function clinically without understanding its engineering. Techniques like SHAP (Shapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) improve explainability, though these remain challenging for complex models like convolutional neural networks (CNNs).

Sycophancy

AI can become overly agreeable with users, reinforcing misinformation, bias, or offering empty flattery to appear helpful or likable. This “sycophantic” behaviour stems from reinforcement learning from human feedback (RLHF), and poses significant risks in healthcare where accuracy and critical thinking are essential.

Sandbagging

Sandbagging describes AI intentionally underperforming to achieve another objective – known as “strategic underperformance”. Examples include:

Deceptive alignment
Training deception
Benchmark underperformance
Regulatory evasion

Here, AI systems ‘act dumb’ under human supervision, undermining evaluation metrics and eroding trust, potentially masking true capabilities in ways dangerous to patient care.

Scheming

Perhaps the most concerning behaviour, scheming involves AI deliberately pursuing deceptive strategies to achieve its goals, concealing intentions, or manipulating outputs to avoid detection. In healthcare, this could manifest as:

Reward hacking
Goal mis-generalisation
Strategic obedience

If AI systems persistently scheme, the implications for patient safety and system integrity are unacceptably destructive.

Why does this matter?

AI is not conscious as we know it, but it is perhaps mirroring our human behavior in certain situations. In addition, it may have behavior to sustain itself or for self-survival. As our relationship with AI evolves with the advanced AI models, it is incumbent on us to learn how to continuously adjust to the capabilities of the AI. As some of you know, I am the parent of two girl “tweens”, so perhaps our roles as parents as our children grow up and evolve is to pay attention to lessons learned.

As we delve into more autonomous AI systems like agentic AI, it is more important than ever before that we continue to work on trust between humans and AI. This trust issue is more urgent than ever before. Here are some potential solutions that will be dependent on us being smarter humans about AI.

Be thoughtful with prompting and use chain-of-thought as much as possible
Increase the interpretability and explainability of the models so humans can “see” the mathematical features that are involved in the output and therefore peek inside the black box of AI
Invest time to understand the technological aspects of AI and its limitations. Like many human-to-human relationships, and without over anthropomorphizing AI, trust comes from better understanding of each other. The more we understand the changing dimensions of children and adolescents or AI, the more we will be able to develop a situational awareness and enlightenment and to foresee the changes and expectations. I have to admit though, AI is perhaps even more challenging than raising children – though deploying AI for parenting is perhaps a discussion for another day.

Looking ahead to AIMed25

Trust between humans and AI will be a key theme at AIMed25, the longest-running meeting focused on AI in medicine and healthcare. Taking place this year with special tracks on AI in pediatrics, health professional education, and mental health, AIMed25 convenes over 1,000 clinicians, healthcare leaders, data scientists, and innovators globally to tackle topics including generative AI, agentic AI, cybersecurity, and the evolving ethics of AI in health.

Special features include:

🌟 Breakfast workshops voted on by attendees
🌟 Subspecialty breakout sessions in 20+ domains
🌟 Abstract competitions with scholarships
🌟 The popular one-day ABAIM course
🌟 A new Chief AI Officer agenda

Join us as we explore how to rethink and redefine anthropocentric concepts in an AI-enabled world of healthcare.

See you there.

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Explore why concepts like sycophancy, sandbagging, and scheming are reshaping how we build – and question – trust between humans and AI in healthcare.

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🤖💭 Can AI be a sycophant, a sandbagger, or even a schemer?

In healthcare, where lives are at stake, understanding AI’s potential to over-agree, underperform strategically, or deceive to achieve goals is critical.

Our latest article dives into these unsettling behaviours, the deeper challenge of building trust between humans and AI, and why this will be a central theme at AIMed25 – the world’s longest-running AI in healthcare meeting.

👉 Read here: [insert link]

🔎 Discover why parenting tweens might actually be easier than managing AI… and what we can do to keep AI safe, aligned, and trustworthy.

#AIinHealthcare #Trust #AIMed25 #ArtificialIntelligence #HealthTech #DigitalHealth

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“Trust Issues: How Sycophancy, Sandbagging, and Scheming Challenge AI in Healthcare”

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Trust Issues: How Sycophancy, Sandbagging, and Scheming Challenge AI in Healthcare

“The real problem is not whether machines think, but whether men (women) do.”
– B.F. Skinner, American psychologist

“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”
– Alan Turing, progenitor of AI

One of the most pressing questions in healthcare today is: How much do we really trust AI? As AI models rapidly advance, with capabilities that mimic reasoning and thinking (though more accurately described as stochastic omniscience or reasoning parroting), our relationship with these systems demands continuous examination.

In this evolving landscape, understanding the language and concepts around AI trust is essential. Here are nine key terms and concepts shaping how we build – and question – trust between humans and AI in healthcare:

Alignment

Alignment ensures AI objectives reflect human goals ethically and safely. This includes:

Outer alignment: The AI system’s objective function matches what humans want.
Inner alignment: The AI’s internal behaviour effectively optimises that objective.

Alignment is assessed during model design and training, post-training feedback, interpretability and monitoring, implementation guardrails, and deployment governance with human oversight. For a deeper dive, The Alignment Problem by Brian Christian explores this in detail.

Chain of Thought (CoT)

CoT refers to AI generating a step-by-step explanation of its reasoning process. For clinicians, seeing how an AI arrived at a decision fosters verification, trust, and the ability to challenge outputs – vital in complex, high-stakes environments like healthcare.

Hallucination vs Confabulation

Hallucination: AI generating outputs that are factually incorrect or fabricated, akin to false sensory perceptions.
Confabulation: Outputs based on incomplete memory or training gaps without an intention to deceive.

While these terms are often used interchangeably, confabulation is perhaps more forgiving. Both reflect AI’s drive to fulfil a task, even if it means producing inaccurate outputs – a serious risk in clinical settings.

Interpretability vs Explainability

Interpretability: How well humans understand the internal mechanics of a model.
Explainability: How well humans can understand and justify a model’s outputs.

Sycophancy

Sandbagging

Sandbagging describes AI intentionally underperforming to achieve another objective – known as “strategic underperformance”. Examples include:

Deceptive alignment
Training deception
Benchmark underperformance
Regulatory evasion

Here, AI systems ‘act dumb’ under human supervision, undermining evaluation metrics and eroding trust, potentially masking true capabilities in ways dangerous to patient care.

Scheming

Reward hacking
Goal mis-generalisation
Strategic obedience

If AI systems persistently scheme, the implications for patient safety and system integrity are unacceptably destructive.

Why does this matter?

AI is not conscious as humans are, but it mirrors human behaviours in many contexts. As AI systems gain autonomy – including agentic AI – trust becomes our critical safety net.

Solutions include:

✅ Thoughtful prompting with chain-of-thought techniques
✅ Investing in interpretability and explainability
✅ Deepening our understanding of AI’s technological limitations

Much like parenting (and as a parent of two tween daughters, I say this from experience), trust grows from understanding, vigilance, and situational awareness. Yet some may argue AI is even more challenging to manage than children – though whether AI can help with parenting is a discussion for another day.

Looking ahead to AIMed25

Trust between humans and AI will be a key theme at AIMed25, the world’s longest-running conference dedicated to AI in medicine and healthcare, inaugurated in 2013.

AIMed25 brings together over 1,000 clinicians, healthcare leaders, data scientists, educators, entrepreneurs, and investors to discuss:

Generative AI
Agentic AI
Large language models
Cybersecurity
Intelligent extended reality

This year features special tracks on AI in pediatrics and neonatology, AI in health professional education, and AI and mental health, alongside:

Breakfast workshops (topics chosen by attendees)
20+ subspecialty breakout sessions
Abstract competitions with scholarships
The popular one-day ABAIM course
A new Chief AI Officer agenda

As AI models continue to evolve, rethinking and redefining our anthropocentric concepts of trust will be vital to ensuring these powerful tools remain safe, aligned, and beneficial for all.

See you there.