Developer Nation Blog

As artificial intelligence (AI) and machine learning (ML) become more deeply woven into the fabric of our daily lives, from healthcare and financial services to self-driving cars, questions around their ethical implications are becoming increasingly urgent.

While ML learning systems are powerful, they can unintentionally perpetuate human biases, impact individual rights and even raise existential risks if not developed with careful consideration.

Unchecked and unregulated AI and ML systems can result in biased algorithms. If algorithms are biased, the information they churn out will be biased and flawed. As a result, people using AI to make decisions with a genuine desire to better humanity may unintentionally violate human rights.

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In this blog, we will explore how AI can be ‘poisoned’ and some of the consequences that can arise from unethical uses of AI and ML, followed by five techniques you can use to ensure you are ethically and responsibly developing your AI and ML models.

AI poisoning and its consequences

AI poisoning is a type of attack aimed at corrupting AI systems. Poisoned AI systems are compromised, which can have severe consequences.

AI or data poisoning happens through the following methods, leading to several ethical risks.

Data injection

Devlopers build AI systems by feeding an AI algorithm data and information about a specific topic. The AI learns about the topic and uses the information to provide new information or make predictions–known as predictive analytics.

A simple illustration of how model poisoning works. (Image source)

For example, a healthcare AI model might be trained on thousands of medical records, symptoms, and treatment outcomes to help it identify patterns and assist doctors in diagnosing illnesses. This process allows the AI to learn from vast amounts of historical data, enhancing its ability to make predictions or recommendations based on similar patterns in new data.

But what happens if the data the AI is learning from is biased? Injecting malicious data distorts and corrupts what the AI model can learn, which generates discriminatory and inaccurate results. In the case of healthcare, it might predict an inaccurate diagnosis.

Mislabeling attacks

Mislabeling attacks involve deliberately altering the labels in the training data, which can cause an AI model to make incorrect associations. For instance, labeling images of horses as cars can lead an image recognition system to misidentify horses as vehicles. This technique introduces hidden biases into the model, skewing its judgment in ways that might not be immediately noticeable but could profoundly impact its performance.

Targeted attack

Targeted attacks aim to manipulate AI models to behave in a specific way for certain inputs while seemingly unaffected overall performance. These attacks create subtle vulnerabilities that are challenging to detect. Such targeted manipulation can result in dangerous, unpredictable behaviors in intelligent systems, particularly in high-stakes applications like self-driving cars or autonomous systems used in the private sector.

Whether done intentionally or unintentionally, AI/data poisoning results in:

Biased decision making

A biased AI system may make discriminatory decisions in areas like hiring, loan approvals, or criminal justice. These decisions reinforce harmful stereotypes and human biases, which threaten civil liberties.

A 2020 study showed Instagram potentially perpetuating harmful body image stereotypes with its AI algorithm, giving pictures of shirtless men or women in their underwear priority over more clothed images. (Image source)

Misinformation and propaganda

Sadly, some bad actors can compromise language models and weaponize them to produce large amounts of misleading or false information. This can be damaging in global or regional processes such as general elections.

In 2016, Facebook allowed access to sensitive user data of 87 million users to consulting firm Cambridge Analytica, which used AI algorithms to micro-target political ads in the 2016 elections in the United States. This raised severe concerns about data privacy and the ethical use of AI in influencing political outcomes.

Privacy violations

Poisoning attacks can also extract or expose sensitive information from AI models. Inadvertently revealing private data due to a compromised model violates individual rights and is an ethical failure. Data privacy is a core principle of responsible AI, and poisoning attacks directly challenge this.

Malicious code injection

Sometimes, poisoned ML models have been shown to act as vectors for malicious code. Attackers could use AI to execute unauthorized actions on users’ systems by inserting code directly into the training process, creating security risks beyond unethical AI use to outright harm.

Data poisoning exemplifies how the exploitation of AI if unprotected, emphasizing the need for ethical principles and rigorous safeguards in AI development.  

Five ways to ensure ethical AI in ML models

As has been demonstrated, ensuring ethical AI when developing models is the responsible thing to do. Here are five techniques that can be employed.

Data collection and preparation

Ethical AI naturally starts at the point of data collection and preparation. Developers working on AI models should ensure they collect diverse data representative of the population the model will serve.

Consider collecting data from a wide range of sources. Sticking with our healthcare AI example, this would mean gathering data on patients from different:

• Hospitals
• Regions
• Populations
• Ages
• Genders
• Races
• Medical histories

In other fields, it might involve collecting data from urban and rural areas, varying income levels, religions, and cultural contexts. The type of data collected depends on the model your are developing. When you use diverse sources, you minimize biased outcomes.

Of course, collecting diverse data isn’t the end of responsible data management. You need to make sure you’ve gathered the necessary approvals and consent. Users should know how you plan to use their data and have the option to opt in or out at any time. For example, suppose you are using AI for customer service (such as through chatbots). In that case, customers should know that their purchase history and previous interactions with the company may be used to train the model.

Here are some methods of collecting data. (Image source)

Additionally, being transparent about how you’re collecting and using data breeds trust. So, suppose you’re a commercial enterprise using a model to serve your e-commerce or finance customers. In that case, transparency can give you a competitive advantage over others who may collect data legally but unethically.

It’s worth noting that collective diverse data doesn’t automatically eliminate bias. Once you have the data, prepare it using techniques like data augmentation (using the data to create new data samples to assess bias) or resampling (re-collecting sample data). This added step helps create a fairer ML model. Bright Data sets a solid example in making transparency and consent key parts of its data collection process.

Data access and security

Ethical AI includes managing how data flows into ML systems. API gateway services play a crucial role by filtering requests, enforcing access policies, and logging interactions to prevent unauthorized data usage.

Businesses can uphold data integrity and transparency by controlling data access and usage through a gateway, mitigating biases, and safeguarding user privacy. This integration of API gateways not only strengthens compliance with ethical standards but also adds a layer of accountability, reinforcing trust in AI-driven solutions.

Another way to uphold data security is through rigorous testing and auditing of ML models.

Security control validation, which thoroughly assesses the effectiveness of safeguards like access restrictions, encrypted data storage, and monitoring systems, helps ensure the integrity of sensitive training data and model outputs.

(Image source)

Conduct this validation process regularly as the security landscape evolves. By prioritizing security alongside ethical AI practices, organizations can have greater confidence that their ML systems behave as intended and do not expose users to undue risk.

AI risk management

Ethical AI models require careful planning to avoid risks like biased predictions or privacy issues. This is where AI risk management becomes essential. It helps organizations identify potential problems early and implement safeguards to keep AI systems transparent and fair.

(Image source)

Wiz.io explains that this approach ensures companies can detect and fix issues, such as unintentional bias or data misuse before they cause harm. Proper risk management also ensures that AI models meet industry standards and build trust with users by being accountable and fair throughout their lifecycle.

Model development

To ensure that AI models make ethical and fair decisions, developers can implement fairness constraints during model training.

Fairness constraints prevent discrimination against specific groups, helping the model avoid skewed outcomes.

Techniques like adversarial debiasing and regularization can be applied, where the model is penalized for biased predictions, encouraging it to treat different groups equitably. These constraints are especially crucial in areas where biased algorithms could impact civil liberties.

Another essential aspect of responsible model development is using interpretable or glass-box AI models whenever possible.

(Image source)

Interpretable models provide a transparent view of their decision-making processes. This transparency helps developers understand how the model reaches specific conclusions, making it easier to detect and address potential biases.

Interpretable models enhance accountability by allowing users to trace each step in the decision-making process, promoting fairness in ML.

For models that require additional clarity, developers can employ explainability techniques such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP).

These methods break down individual predictions and offer insights into the model’s overall behavior, enabling a deeper understanding of how various factors influence outcomes. 

Another way to evaluate fairness in model development is using humans. Encourage team members and the general public (who represent your target audience) from diverse backgrounds to provide input on your model’s outputs.

Monitoring and evaluating models

Regular ethical reviews play a crucial role in monitoring and evaluation. These reviews involve periodic audits assessing the AI system’s alignment with desired ethical principles. These reviews are particularly important for evaluating the model’s impact on vulnerable or marginalized groups, helping to identify and address any unintended consequences that may arise over time.

Continuous monitoring in real-world scenarios further reinforces ethical alignment, providing insight into how the model performs under real-life conditions and enabling swift adjustments if ethical standards are compromised. Establishing clear ethical guidelines or a standard operating procedure (SOP) helps ensure that these reviews and monitoring practices are consistently applied, creating a robust framework for ethical AI management.

Wrapping up

Technological advances are exciting. The AI explosion is akin to the Industrial Revolution, and we are fortunate to live in an era when we see advances happening right before our eyes.

However, progression always comes with challenges and risks, and our responsibility is not to be swayed by technology at the expense of ignoring threats to our human rights.

This blog has examined what can happen when things go wrong and offered techniques to minimize harm.

Enjoy using AI to superpower your business—but be responsible!

Author bio

Guillaume is a digital marketer focused on handling the outreach strategy at uSERP and content management at Wordable. Outside of work, he enjoys his expat life in sunny Mexico, reading books, wandering around, and catching the latest shows on TV.