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Case study ensuring AI tool accuracy, fairness and robustness.

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A case study of media's diagnostic system.

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AI system accuracy and effectiveness must be rigorously tested post-deployment to ensure reliability,

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fairness, and robust performance in dynamic environments.

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Consider the case of Medhai, a healthcare technology company that recently deployed an AI powered diagnostic

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tool to assist doctors in identifying early signs of diabetic retinopathy.

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Sarah, the lead data scientist at Medai, along with her team, gathers data from various hospitals

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where the AI tool has been deployed.

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They collect input variables like patient demographics, retinal images, predicted outcomes, and actual

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outcomes by comparing predicted and actual outcomes.

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Sarah's team assesses the tool's accuracy.

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However, they recognize that accuracy alone is insufficient.

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They also calculate precision recall, F1 score, an O SoC to provide a more comprehensive performance

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assessment.

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In this medical context, achieving high recall is crucial since failing to identify a true positive

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could have serious consequences.

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Reflecting on the first phase, Sarah wonders how can they ensure that the AI tool maintains a balance

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between recall and precision?

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They decide to prioritize recall, but closely monitor precision to avoid an overwhelming number of

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false positives, which could lead to unnecessary treatments.

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Next, the team delves into error analysis.

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They categorize errors into false positives and false negatives.

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False positives are cases where the AI incorrectly predicts diabetic retinopathy, while false negatives

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are misdiagnoses.

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Understanding these errors helps in identifying weaknesses in the AI tool.

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For instance, they discover a higher rate of false negatives in patients with atypical retinal images.

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This insight prompts Sarah to ask what specific features in the retinal images are causing these errors.

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To address this, they employ more detailed image analysis techniques and find that certain features,

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like unusual blood vessel patterns, are not well represented in the training data.

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This leads them to retrain the model with more diverse retinal images, aiming to reduce the false negative

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rate.

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Sarah then turns her focus to bias detection.

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Given that the AI tool was trained on historical data, there is a risk of inherent biases such as demographic

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biases.

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The team conducts a fairness audit and discovers that the tool has a slightly higher error rate for

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patients from underrepresented ethnic groups.

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This raises an important question how can the team mitigate these biases to ensure fair treatment across

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all demographic Graphic groups.

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They implement fairness aware machine learning techniques to adjust the training process, ensuring

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that the model gives equal importance to all demographic groups.

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Additionally, they validate the changes by running the AI tool on a balanced test dataset, confirming

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that the biases have been mitigated.

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Robustness testing is another critical step.

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Sarah's team conducts stress testing and adversarial testing to evaluate the AI tools stability under

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various conditions.

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They intentionally introduce slight perturbations to retinal images to see how the AI tool responds.

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Surprisingly, they find that minor changes significantly impact the model's predictions.

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This vulnerability to adversarial attacks prompts Sarah to question what measures can they take to enhance

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the model's robustness against such perturbations.

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To counter this, they incorporate adversarial training into the model development process, where the

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AI tool is trained on both clean and perturbed images.

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This approach helps in developing a more robust model that can handle variations in input data.

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Continuous monitoring is crucial for maintaining the AI tools effectiveness over time.

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The team sets up a monitoring system that tracks performance metrics and flags any significant deviations.

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For instance, they notice a gradual decline in accuracy over several months.

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Sarah hypothesizes that this could be due to concept drift, where the data distribution changes over

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time.

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To adapt to these changes, the team uses online learning techniques that allow the model to update

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itself with new data continuously.

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This adaptive approach ensures that the AI tool remains effective even as patient demographics and medical

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practices evolve.

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Clear documentation and reporting are essential for transparency and accountability.

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Sarah ensures that every step of the post-hoc testing process is meticulously documented.

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This includes performance metrics, error analysis, bias detection, and robustness evaluation.

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Comprehensive documentation supports communication with stakeholders, including developers, users,

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and regulatory bodies.

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For example, to comply with the EU General Data Protection Regulation.

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The team maintains detailed records of the AI tools, decision making processes, ensuring transparency

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and accountability.

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Finally, integrating post-hoc testing insights into the broader AI system lifecycle is critical.

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Sarah establishes a feedback loop where insights gained from post-hoc testing inform future iterations

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of the AI tool.

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For instance, the additional diverse retinal images used to reduce false negatives, and the fairness

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aware machine learning techniques are incorporated into the model retraining process.

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This iterative improvement cycle helps the AI tool evolve, becoming more accurate, effective, and

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fair over time.

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Reflecting on the process, Sarah considers the broader implications of their findings.

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How can they generalize these practices to other AI applications within Medai, ensuring that these

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rigorous post-hoc testing procedures become standard practice across the company could significantly

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enhance the reliability and fairness of all their AI tools.

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Through this detailed case study, we've explored various dimensions of post-hoc testing for AI system

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accuracy and effectiveness.

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Validating model predictions requires a comprehensive approach encompassing error analysis to uncover

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and address specific weaknesses.

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Bias detection and mitigation.

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Ensure fairness while robustness testing guards against vulnerabilities.

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Continuous monitoring and adaptive learning maintain performance over time.

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Clear documentation underpins transparency and regulatory compliance, and integrating insights into

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the lifecycle fosters ongoing improvement.

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By addressing each question raised, we see how critical thinking and systematic analysis lead to practical

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solutions.

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Balancing recall and precision.

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Identifying specific error sources.

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Mitigating biases, enhancing robustness, adapting to concept drift, and ensuring transparency collectively

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contribute to the responsible deployment of AI technologies.

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This case study not only illustrates the importance of post-hoc testing, but also provides a framework

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for students to apply these principles in real world scenarios, enhancing their understanding and application

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of the lesson material.