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Ethics & Policy

The Ethics of Using AI in Healthcare

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2 months ago

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The Greek physician and philosopher Hippocrates ushered in the concepts of modern medicine, establishing medicine as a science based on careful observation of patient symptoms and empirical data rather than superstition. But Hippocrates did more than simply separate medicine from magic; he is credited with adding ethics to the science of medicine, as laid out in the Hippocratic Oath, which states, in part, “First, do no harm.” Roughly 2,500 years later, physicians take the Hippocratic Oath as a pledge to care for patients, ease suffering, wisely use their knowledge and judgment, and avoid causing harm.

Modern physicians now have an extraordinary array of tools and technologies at their disposal, and the dramatic rise of artificial intelligence (AI) promises powerful new analytical and diagnostic capabilities. AI is an advanced computing technology that can process vast amounts of medical data, drawing on the collective knowledge of countless physicians and practitioners across continents and centuries. AI technologies used in healthcare have the potential to render earlier, faster, more accurate diagnoses; perform real-time patient monitoring; and predict pathways to the most effective drug and procedural treatments to achieve superior patient care.

But the rush to embrace technology and bring AI systems into the healthcare mainstream carries serious ethical concerns. Sensitive user data in other industries (such as social media) is routinely collected and used to shape advertising, affect public opinion and generate revenue — often without permission from the users involved and with little regard for how these disclosures might affect them.

Consequently, the healthcare industry faces critical ethical considerations when using AI systems. First, it is responsible for ensuring that sensitive patient data is protected in accordance with prevailing regulatory obligations, such as patient data privacy standards. Second, it is responsible for ensuring that patient data is used appropriately so that healthcare providers and AI systems “do no harm.”

Why do ethics matter when using AI in healthcare?

Ethical issues related to the use of AI in healthcare carry both technical and moral implications. To do their jobs, professional healthcare practitioners use extensive knowledge, which in turn must be applied fairly and equitably for the benefit of all patients. AI is far from perfect, so providers must take care to apply ethical constructs to AI use. Ethical matters in the AI application of medical knowledge focus on three principal areas.

1. Accuracy

AI systems can sift enormous amounts of data to find trends and proffer diagnoses much faster than human practitioners, but they must be trained using examples of known conditions, as AI can only know what it’s taught. Further, the correctness of AI conclusions must be reinforced and optimized by feedback from practitioners. AI makes mistakes and has the potential to make up false conclusions — a phenomenon called AI hallucination. This means AI assistance in healthcare should never be taken at face value. Its information can save lives, but its conclusions should always be examined and validated by human experts.

2. Fairness

AI conclusions are only as good as the underlying data. Unfortunately, data is often imperfect, with flaws that include incomplete, inaccurate and biased data. These flaws can negatively affect AI decision-making, lowering the accuracy of its conclusions — particularly if the data underrepresents patients based on social class, race, gender, religion, sexual orientation or disabilities. This makes data quality and bias mitigation central issues in AI development and training.

3. Security

AI is noted for its ability to access and process enormous amounts of data and to act upon its conclusions with a startling amount of autonomy. This puts tremendous pressure on data security needed to safeguard sensitive patient data from illegitimate or inappropriate access — by the AI as well as AI users — and to ensure that data used to train AI systems is anonymized properly so that conditions and outcomes cannot be coupled to specific patients. Security issues are partly the domain of IT professionals, but dealing with AI security requires a holistic approach that embraces practitioners, administrators and researchers. Also, it must be reinforced with ongoing awareness training and policy development.

Ethical considerations when using AI in healthcare

There are four key ethical considerations for ensuring AI-driven healthcare businesses use AI tools wisely and for the benefit of patients. Here’s how to address them.

Creating and enforcing ethics policies

Detailed policy frameworks are needed to guide the use of AI systems, translating AI recommendations into clinical practice and ensuring AI transparency and explainability. Policies emphasize the ethical use of AI technology to benefit patients while preventing patient harm or malfeasance. Such policies often include discussions of factors such as patient consent, equal access to care, AI errors, AI bias and outright AI system misuse. Policies sometimes include discussions of patient contact to ensure that AI use does not dehumanize patients and that individual AI recommendations are both accurate and reliable before they are enacted.

Maintaining data security and patient privacy

AI can access and process bewildering amounts of data, which must be stored and identified. With strong regulatory and legislative statutes already in place to safeguard patients’ personally identifiable information, ethical requirements will include factors such as getting patient consent regarding how data is collected and used, collecting minimal data, following data storage and security protocols (such as end-to-end encryption), using identity and access management (IAM) tools and implementing data backup and disaster recovery measures. Ethical considerations should also explore ways to prevent and mitigate events such as unauthorized AI system use.

Applying human oversight to AI recommendations

It’s easy to simply trust the AI system and implement its outcomes, but blind trust in automation — especially in systems where errors and biases are present — can have devastating consequences for patients and healthcare organizations. Ethical considerations will detail the need and application of human oversight at several levels. For example, practitioners should review AI recommendations for accuracy and validate the AI decisions by double-checking the explainability of the outcome and considering subjective issues such as patient preferences and values. Further, there should be careful consideration of liability and other legal concerns that affect the entire AI system chain, including AI system developers, AI trainers, clinicians, administrators and other staff. Regular training in the proper and acceptable use of AI tools is part of this consideration.

Ensuring a positive patient experience

Ethics also extends to patient treatment at several levels, including having empathy and sensitivity in information-gathering, recognizing and respecting patient preferences and values, and following up on patient outcomes as well as perceived quality of care. Part of patient involvement also includes clear and concise patient consent, which delineates the information collected, why it’s needed, and how it’s used — including further AI training, if needed — and allowing patients to opt out of certain data uses.

Challenges of ethical integrations

Ethics are central to all types of healthcare practices, but integrating ethical concerns into AI systems can present several broad challenges for organizations, including four major obstacles outlined below.

1. Lack of leadership

A core problem with AI and ethics is the notion that ethical issues are addressed as a native part of the AI platform — in other words, that simply using an AI platform will correlate to ethical practices. This is not the case. AI systems have no automatic ethical or moral direction and will perform in any way they are utilized. Consequently, the ethical use of AI must start at the top of the healthcare organization, with senior leadership recognizing the need for adherence to ethical standards and issuing the mandate for ethical use of AI systems.

Healthcare leadership seeking a successful integration of ethics and AI will typically focus on understanding the risks of AI related to factors including data security, data ownership, data quality, data bias, informed patient consent, accountability and liability. Once the salient risks are understood, the leadership team can craft policies to guide the implementation and use of AI systems.

2. Lack of policies

Ethical use of AI in healthcare relies on carefully considered policies that address issues such as data security and patient data protection, data retention, clinical validation standards (i.e., ensuring that the AI is correct in its conclusions), data quality and bias mitigation. Policies lay out the rules for using AI systems and their underlying data in accordance with ethical healthcare standards. Policies also help ensure compliance with regulatory and liability obligations.

However, healthcare organizations often fall short when developing, maintaining and educating practitioners on those policies. Absent or incomplete policies lead to unacceptable AI use, put sensitive patient data at risk and potentially expose the organization to regulatory or legal jeopardy.

3. Lack of training

Establishing governance policies around ethical AI use has little value if those policies are not communicated and reinforced across the healthcare organization. Just as everyday businesses establish data security or acceptable use policies and provide regular training to staff, so healthcare organizations must translate AI ethics policies into practical training that can serve new and veteran employees.

Simply providing employees with a copy of the policy isn’t enough — the risks to the healthcare organization (and its patients) are simply too great. The organization must design and implement meaningful AI ethics training and make that training mandatory for all clinical and administrative staff.

4. Lack of expertise

Implementing AI ethical standards in a healthcare setting can be daunting, requiring extensive knowledge of AI systems and detailed insight into the organization’s computing infrastructure. For example, IT must implement the mechanisms needed to protect stored data (such as IAM or data encryption); data scientists must work diligently to ensure data quality and mitigate bias for both training and practical usage; and practitioners must recognize and protect sensitive patient data. In addition, AI system experts must ensure transparency and explainability in the AI system to demonstrate comprehensive understanding of AI behavior.

All of this demands an expert staff that understands and supports ethical integration efforts with AI systems. A gap in expertise, such as a lack of explainability in AI system behavior, can compromise AI ethics initiatives and put the healthcare organization at risk.

The risks of AI bias in healthcare

AI operates by processing data against a series of algorithms that have been trained in advance using example data. Unfortunately, the same types of bias experienced by human decision-makers can also be conveyed to an AI system through the design of the algorithms and the data sets used to prepare those algorithms for production. While bias can be detrimental in any AI platform, bias in AI can have profound effects on entire patient demographics by under-representing potential conditions or offering suboptimal patient outcomes.

A primary focus of any AI system development is the identification and mitigation of system bias by measures such as collecting comprehensive data from diverse and highly inclusive sources. Common types of AI bias include the following:

  • Evidence bias (also known as publication or reporting bias). Ideally, AI data is objective and transparent, but evidence bias occurs when external factors work to skew data that eventually feeds AI systems. For example, funded research can produce bias as outcomes might skew in favor of funding sources. The same impact occurs when research seeks positive results, sometimes resulting in data skew that favors positive results.
  • Experience bias (also known as annotation or labeling bias). Data science and clinical professionals can introduce inconsistent or incorrect data labeling and classifications, essentially introducing undesirable skew into training data as well as algorithm training and tuning tasks. Even clinicians responsible for patient data classification can inadvertently cause such bias.
  • Environment bias. This is a variation of exclusion bias where social, physical and environmental factors related to the data sets are not collected and included to add vital context to patient data. For example, environmental bias can occur when details such as residence location, living conditions, income and education are overlooked in the total data set.
  • Exclusion bias. Data collection and processing are systematically ignored or underrepresented. For example, if data that pertains to a specific patient demographic is omitted from the total data set, the AI will provide poorer performance for those patients. This can lead to outcomes such as missed or incorrect diagnoses and erroneous treatment recommendations.
  • Empathy bias. Empathy bias can occur when subjective, emotional considerations — often qualitative information that is almost impossible to quantify — are omitted from data sets and AI training. This prevents the AI from bringing context and nuance to its decision-making and its consideration of the unique needs of patients and groups. For example, not including patient preferences, such as end-of-life wishes, and moral or cultural values can drive inappropriate recommendations.

There are three strategies used to address AI bias. First, ensure that data sets represent diverse and inclusive sources with extensive examples and variabilities. Second, ensure explainability so that AI decision-making is well understood and trustworthy. And third, use comprehensive monitoring to gauge AI outcomes and look for bias over time as the AI system continues to receive new data and learn.

Stephen J. Bigelow, senior technology editor at Informa TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.



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Ethics & Policy

AI and ethics – what is originality? Maybe we’re just not that special when it comes to creativity?

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10 hours ago

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July 7, 2025

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I don’t trust AI, but I use it all the time.

Let’s face it, that’s a sentiment that many of us can buy into if we’re honest about it. It comes from Paul Mallaghan, Head of Creative Strategy at We Are Tilt, a creative transformation content and campaign agency whose clients include the likes of Diageo, KPMG and Barclays.

Taking part in a panel debate on AI ethics at the recent Evolve conference in Brighton, UK, he made another highly pertinent point when he said of people in general:

We know that we are quite susceptible to confident bullshitters. Basically, that is what Chat GPT [is] right now. There’s something reminds me of the illusory truth effect, where if you hear something a few times, or you say it here it said confidently, then you are much more likely to believe it, regardless of the source. I might refer to a certain President who uses that technique fairly regularly, but I think we’re so susceptible to that that we are quite vulnerable.

And, yes, it’s you he’s talking about:

I mean all of us, no matter how intelligent we think we are or how smart over the machines we think we are. When I think about trust, – and I’m coming at this very much from the perspective of someone who runs a creative agency – we’re not involved in building a Large Language Model (LLM); we’re involved in using it, understanding it, and thinking about what the implications if we get this wrong. What does it mean to be creative in the world of LLMs?

Genuine

Being genuine, is vital, he argues, and being human – where does Human Intelligence come into the picture, particularly in relation to creativity. His argument:

There’s a certain parasitic quality to what’s being created. We make films, we’re designers, we’re creators, we’re all those sort of things in the company that I run. We have had to just face the fact that we’re using tools that have hoovered up the work of others and then regenerate it and spit it out. There is an ethical dilemma that we face every day when we use those tools.

His firm has come to the conclusion that it has to be responsible for imposing its own guidelines here  to some degree, because there’s not a lot happening elsewhere:

To some extent, we are always ahead of regulation, because the nature of being creative is that you’re always going to be experimenting and trying things, and you want to see what the next big thing is. It’s actually very exciting. So that’s all cool, but we’ve realized that if we want to try and do this ethically, we have to establish some of our own ground rules, even if they’re really basic. Like, let’s try and not prompt with the name of an illustrator that we know, because that’s stealing their intellectual property, or the labor of their creative brains.

I’m not a regulatory expert by any means, but I can say that a lot of the clients we work with, to be fair to them, are also trying to get ahead of where I think we are probably at government level, and they’re creating their own frameworks, their own trust frameworks, to try and address some of these things. Everyone is starting to ask questions, and you don’t want to be the person that’s accidentally created a system where everything is then suable because of what you’ve made or what you’ve generated.

Originality

That’s not necessarily an easy ask, of course. What, for example, do we mean by originality? Mallaghan suggests:

Anyone who’s ever tried to create anything knows you’re trying to break patterns. You’re trying to find or re-mix or mash up something that hasn’t happened before. To some extent, that is a good thing that really we’re talking about pattern matching tools. So generally speaking, it’s used in every part of the creative process now. Most agencies, certainly the big ones, certainly anyone that’s working on a lot of marketing stuff, they’re using it to try and drive efficiencies and get incredible margins. They’re going to be on the race to the bottom.

But originality is hard to quantify. I think that actually it doesn’t happen as much as people think anyway, that originality. When you look at ChatGPT or any of these tools, there’s a lot of interesting new tools that are out there that purport to help you in the quest to come up with ideas, and they can be useful. Quite often, we’ll use them to sift out the crappy ideas, because if ChatGPT or an AI tool can come up with it, it’s probably something that’s happened before, something you probably don’t want to use.

More Human Intelligence is needed, it seems:

What I think any creative needs to understand now is you’re going to have to be extremely interesting, and you’re going to have to push even more humanity into what you do, or you’re going to be easily replaced by these tools that probably shouldn’t be doing all the fun stuff that we want to do. [In terms of ethical questions] there’s a bunch, including the copyright thing, but there’s partly just [questions] around purpose and fun. Like, why do we even do this stuff? Why do we do it? There’s a whole industry that exists for people with wonderful brains, and there’s lots of different types of industries [where you] see different types of brains. But why are we trying to do away with something that allows people to get up in the morning and have a reason to live? That is a big question.

My second ethical thing is, what do we do with the next generation who don’t learn craft and quality, and they don’t go through the same hurdles? They may find ways to use {AI] in ways that we can’t imagine, because that’s what young people do, and I have  faith in that. But I also think, how are you going to learn the language that helps you interface with, say, a video model, and know what a camera does, and how to ask for the right things, how to tell a story, and what’s right? All that is an ethical issue, like we might be taking that away from an entire generation.

And there’s one last ‘tough love’ question to be posed:

What if we’re not special?  Basically, what if all the patterns that are part of us aren’t that special? The only reason I bring that up is that I think that in every career, you associate your identity with what you do. Maybe we shouldn’t, maybe that’s a bad thing, but I know that creatives really associate with what they do. Their identity is tied up in what it is that they actually do, whether they’re an illustrator or whatever. It is a proper existential crisis to look at it and go, ‘Oh, the thing that I thought was special can be regurgitated pretty easily’…It’s a terrifying thing to stare into the Gorgon and look back at it and think,’Where are we going with this?’. By the way, I do think we’re special, but maybe we’re not as special as we think we are. A lot of these patterns can be matched.

My take

This was a candid worldview  that raised a number of tough questions – and questions are often so much more interesting than answers, aren’t they? The subject of creativity and copyright has been handled at length on diginomica by Chris Middleton and I think Mallaghan’s comments pretty much chime with most of that.

I was particularly taken by the point about the impact on the younger generation of having at their fingertips AI tools that can ‘do everything, until they can’t’. I recall being horrified a good few years ago when doing a shift in a newsroom of a major tech title and noticing that the flow of copy had suddenly dried up. ‘Where are the stories?’,  I shouted. Back came the reply, ‘Oh, the Internet’s gone down’.  ‘Then pick up the phone and call people, find some stories,’ I snapped. A sad, baffled young face looked back at me and asked, ‘Who should we call?’. Now apart from suddenly feeling about 103, I was shaken by the fact that as soon as the umbilical cord of the Internet was cut, everyone was rendered helpless. 

Take that idea and multiply it a billion-fold when it comes to AI dependency and the future looks scary. Human Intelligence matters



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Ethics & Policy

Preparing Timor Leste to embrace Artificial Intelligence

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12 hours ago

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July 7, 2025

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UNESCO, in collaboration with the Ministry of Transport and Communications, Catalpa International and national lead consultant, jointly conducted consultative and validation workshops as part of the AI Readiness assessment implementation in Timor-Leste. Held on 8–9 April and 27 May respectively, the workshops convened representatives from government ministries, academia, international organisations and development partners, the Timor-Leste National Commission for UNESCO, civil society, and the private sector for a multi-stakeholder consultation to unpack the current stage of AI adoption and development in the country, guided by UNESCO’s AI Readiness Assessment Methodology (RAM).

In response to growing concerns about the rapid rise of AI, the UNESCO Recommendation on the Ethics of Artificial Intelligence was adopted by 194 Member States in 2021, including Timor-Leste, to ensure ethical governance of AI. To support Member States in implementing this Recommendation, the RAM was developed by UNESCO’s AI experts without borders. It includes a range of quantitative and qualitative questions designed to gather information across different dimensions of a country’s AI ecosystem, including legal and regulatory, social and cultural, economic, scientific and educational, technological and infrastructural aspects.

By compiling comprehensive insights into these areas, the final RAM report helps identify institutional and regulatory gaps, which can assist the government with the necessary AI governance and enable UNESCO to provide tailored support that promotes an ethical AI ecosystem aligned with the Recommendation.

The first day of the workshop was opened by Timor-Leste’s Minister of Transport and Communication, H.E. Miguel Marques Gonçalves Manetelu. In his opening remarks, Minister Manetelu highlighted the pivotal role of AI in shaping the future. He emphasised that the current global trajectory is not only driving the digitalisation of work but also enabling more effective and productive outcomes.



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Ethics & Policy

Experts gather to discuss ethics, AI and the future of publishing

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20 hours ago

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July 7, 2025

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Representatives of the founding members sign the memorandum of cooperation at the launch of the Association for International Publishing Education during the 3rd International Conference on Publishing Education in Beijing.CHINA DAILY

Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.

True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing’s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.

This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.

Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.

Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.

BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.

Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.

Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.

Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.

Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.

The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.

 

 

 



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