Comparative Intelligence and Ethics in Machine Learning Systems

Jul 11, 2026 15:09 · 29:15 · English · Whisper Turbo · 2 Speakers
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S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
Hello,
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Dr.
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Oladako.
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It's Ben here.
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I'm here to do a presentation on the
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machine learning module.
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And the topic of the presentation is comparative intelligence and ethics
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in machine learning systems.
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This is the agenda of my presentation.
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I will look at introduction,
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purpose of the assignment,
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dataset selection and pre -processing,
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selected application track,
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model design,
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where I look at machine learning and deep learning.
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On this topic,
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I will look at evaluation matrices,
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validation strategy,
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explainability findings.
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And then I'll also go and look at ethical and bias consideration,
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deployment consideration,
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model demonstration and choice of tools used,
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conclusion,
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recommendation,
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and then I'll also look at the references used on the presentation.
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On the introduction,
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I just want to say that machine learning
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systems have become central to modern decision
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making.
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From healthcare diagnosis to financial features,
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this is supported by REN and others on their paper of
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2024.
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As these systems evolve towards greater autonomy
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and complexity,
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questions arise where one wants to check their intelligence compared
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to human reasoning and also check their ethical principles.
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that needs to be embedded.
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on the on their system design the presentation will look at some of these points
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but added on top of this will be other points while some of the points
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that i will look at it would be the entire intersection of computational
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intelligence and moral responsibility that's what i want to investigate
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examine how in a different ml models both classical
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and deep learning reflect varying degrees of interpretability accountability
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and fairness and look at algorithm decision making
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and transparency thereof.
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On the
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purpose of assignment,
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I want to indicate that this assignment will critically examine
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how different machine learning architectures demonstrate varying
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forms of intelligence and ethical alignment.
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It aims to compare classical algorithms from machine learning
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and deep learning models in terms of the following.
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So we will look at these dimensions.
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the transparency,
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efficiency,
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and explainability,
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fairness, and accountability,
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where this will be looked against the classical ML
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models and the deep learning.
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And these are the characteristics of how these models behave when
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you compare them across these four dimensions.
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Data set selection and pre -processing.
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The data set that I've selected is the tuberculosis,
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the TB data set.
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The reasons why this was chosen is because TB remains
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as a major global health issue,
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and the chest X -rays are a primary diagnostic tool
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to diagnose TB,
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use X -rays,
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and using the data set that was downloaded from cackle .com.
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This TB dataset allows ML and DL models
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to tackle a real -world medical challenge.
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And the reason why this dataset was selected is as follows.
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The dataset itself,
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it has a clinical relevance.
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Why?
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Because TB is a global health issue and a challenge.
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So doing this dataset will help a lot.
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It has X -rays.
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It already has X -rays.
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diagnosis it is it has a large scale availability it has
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a lot of of x -rays embedded on the on the data set itself
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thousands and thousands of them it also support deep learning models
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so it is very key for this presentation it also has a balanced representation
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why balance presentation a representation because it has both the
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positive cases and the negative cases for tb it also supports
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a supervised models and then
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It has a good potential on benchmarking
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because it is widely cited in many research papers,
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so it is very relevant for this presentation.
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It also supports the comparison of computer vision methods.
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And lastly,
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it has a strong public accessibility.
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It is an open source data set,
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so it is very accessible,
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and it also promotes reproducibility.
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and collaboration as it is an open source dataset.
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Dataset selection and preprocessing.
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Dataset preprocessing was done
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on the data set to carry
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out and prepare the structure activities before the data
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analysis on both the traditional LM and
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the deep learning.
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On the traditional LM,
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these are the pre -processing activities that were applied,
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grayscaling,
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noise reduction,
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histogram equalization,
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dimensional reduction.
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and then on the deep learning resizing was done to standardize the input
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size normalization to scale down the pixels values for
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stable for stable training and data augmentation
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The next slide talks to the selection of the application track.
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There were two tracks that were given to us to select from,
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and we were given the computer vision and the recommender
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systems.
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I just want to indicate that before one chooses a
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tracker,
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One need to make sure that the tracker that
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you choose will depend on the type of problems that one wants
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to solve and the impact that that tracker
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will have on the industry that you aim to analyze.
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So I did a comparison between the two trackers,
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the computer vision and the recommender systems,
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and then the focus on the...
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CVS,
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the computer vision.
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I checked the focus.
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This tracker always focuses
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on image and video analysis.
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Where is it applicable?
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It is always applicable on self -driven cars,
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medical imaging,
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and industrial automation.
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The workflow,
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you get the images as an input.
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You do object detection,
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you do analysis,
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and then you provide
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and the understanding of the output as the results.
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The strength of the CV model,
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it is very strong.
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because it is used well for interaction,
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like I indicated,
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on self -driven cars.
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And it also has a very strong support for robotics
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and safety.
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The recommender systems,
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where is their focus?
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Their focus is on personalized recommendation.
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Where is it applicable?
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It is mostly applicable on e -commerce,
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streaming services like Netflix.
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social network like WhatsApp.
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And the workflow,
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you get user data,
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then you process that data,
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then based on the data,
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you choose the preferred model,
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then you analyze the data,
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and then you provide the recommendation as outputs.
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And then the strength,
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where is it strong?
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It is very strong on user engagement,
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like on social media.
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It is also very strong on sales and retention.
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Based on the comparison above,
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computer vision was chosen for this presentation because the
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data set that is used on this presentation is medical images,
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and then it is discovered that CV models on medical images
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is one of the most transformative applications of AI
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that bridges healthcare and technology to improve diagnosis,
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treatment,
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and patient outcome.
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This is supported by Esteva.
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and others on their paper of 2021.
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Model design.
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On the model design,
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I looked at the valuation matrices on
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the traditional ML.
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machine learning model,
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I chose the support vector machines,
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the SVM model with handcrafted features.
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On the deep learning model,
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I chose the convolutional and neural network,
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the CNN.
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Now looking at the results,
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classification matrix is one of the results using the SVM
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on ML.
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It has these classifications.
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the cases where there's no TB,
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zero cases,
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cases where there's TB and accuracy and macro average and weighted
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average.
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Looking at the cases with TBU,
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it used 706 features.
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On the cases with TB,
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it used 134.
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And then one conclude,
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looking at these results,
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one concluded that the SVM model is excellent in predicting
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cases where there's no TB.
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but reasonably good in predicting cases where there's TB.
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The overall accuracy is around 95%,
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but this shows a slightly lower scores on the classes
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where there's TB.
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And then this suggests that the model struggles a bit with the minority detection.
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But this is common.
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This is a common issue in data sets that
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have imbalances,
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especially with the X -ray data set.
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continue with the results now we look at
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the CNN model,
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and then it used 10 epochs to
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train the model.
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And when you look at the accuracy,
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the accuracy is 100 % across the 10 epochs,
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and then the loss is also zero across the epochs.
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What does that mean?
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That indicates that the model has no error on training
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the data,
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on training the model,
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and it also predicts perfectly.
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with the match labels now comparing both now the the models
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the svm from ml models the cnn from
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the deep learning
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Compare the results across these aspects,
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the accuracy,
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the loss,
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the class balance,
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and then see how the tool performed.
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On the accuracy,
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the overall performance of the SVM is around 95%.
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And then on the CNN is
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around 100 % training.
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And then the loss is zero for CNN,
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which is a perfect fit.
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svm is not strong but definitely it is greater than zero on class
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balance it is different discovered that it is very strong on the majority
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where there are no no tb cases but very weak but a bit
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weak on the on the minority where there's tb to classify those and
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to predict those and then the on the on the same end there's no distinction
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it looks like everything is perfect on the generalization the
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generalization of the results it is moderate
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to with realistic errors,
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but on the CNN,
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it looks like it has a high risk of overfitting
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the model.
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On the interpretation of the results,
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it is really reliable,
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but imperfect.
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The CNN,
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it is very suspicious.
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One needs to further conduct further analysis,
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like validation or check metrics.
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Continuing with the model design,
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I'm looking at the validation strategy.
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I compared both the SVM model
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and the CNN model using the K -fold method.
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Then look at the accuracy and the accuracy
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of the CNN.
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The CNN was trained on four training folds,
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and the accuracy of those five folds
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training folds,
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it is between 95 % and 98%.
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of the five folds,
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training folds,
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and then the average is 97%.
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Comparing the two,
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it shows that the CNN model performed better
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than the SVM because it is 97 % while
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the SVM is sitting at 94%.
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Now,
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what that says is...
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the medical imaging tasks such as TB chest x
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-rays classification where data sets are often limited and in balance.
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Robust validation is critical for building confidence that
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both SVM and CN models will perform consistently
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in real -world scenarios.
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So it is very important to perform this validation using the k
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-fold method to ensure that both this model will perform.
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accurate on the real -world scenarios.
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The validation in this case is very important based
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on the following reasons.
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It ensures that the model performs performance
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reflects the ability to generalize to unseen data rather
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than just the memorization during the training set.
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It also makes sure that using strategies like K -Fault
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every data point is tested across different splits giving
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a more reliable estimate and accuracy.
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This process helps in detecting overfit like
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in the CNN model where model just performs very well
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on training data but fails when it comes to the real sample.
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In summary on this method
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One can say that validation acts as a safeguard that distinguishes
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genuine learning from memorization,
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ensuring that models perform reliably in real -world
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scenarios rather than just excelling on training datasets.
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Now, going to the explainability findings,
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I need to introduce this topic just to say that explainability
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in the machine learning and deep learning models,
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especially when one uses the SVM and the CNN model,
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is very essential for transparency,
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trust, and ethical deployment.
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So I applied sharp on the SVM
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model.
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to check explainability.
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So the sharp summary plot on the right here highlights how
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different features contributed to the model's prediction.
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So if you look at this one,
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it shows that a feature like 136.
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337,
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505,
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and 3094 have
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negative sharp values,
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meaning that they generally reduce the predicted output rather
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than increasing it.
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Overall,
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on this visualization,
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One can say that the SVM model is heavily driven by few key
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features with their values consistently pulling the prediction
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downwards instead of pushing it downwards.
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One then can conclude that it is critical for one to understand.
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This plot helps
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us to understand the model behavior,
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the diagnosis biases.
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and to ensure that transparency is there during
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deployment.
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Now looking at explainability for the CNN
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model, I applied the GRANDCAM
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method and these are the results that came from the
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GRANDCAM method when
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applied on the CNN.
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And this is applied on the chest X -rays,
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which visually explains how the CNN model makes prediction.
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So when one looks at this,
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you see there are two different colors.
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The dark blue colors,
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these are the areas where there's high activation.
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And this shows that this is where the model forecast
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when it was doing its predictions.
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on the other hand you see these yellow colors here and these
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yellow colors are the areas where there's low activation
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and these are the cases where there's little contribution to
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the cnn output
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One can then conclude that this visualization is very important because
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it provides us with the explainability of the model showing
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clinicians and researchers where the model forecast when
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it was doing its prediction for judgment.
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The next slide,
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look at ethical and bias consideration.
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Bias consideration,
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these are the items that I looked at.
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I looked at hidden disparities for overall accuracy
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to make sure that the subgroups are masked correctly
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to cater for differences.
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Computer vision on bias,
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it's very important,
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especially for CNN because CNN might perform very well.
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on on on on light images but struggles on
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the on the low contrast images and then recommend
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recommender systems these models will will perform very
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well on high activity users compared to low user activities
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so one needs to take that care when you do the the the bias
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one subgroup analysis
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The results need to be broken down into demographic,
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environment and behaviour factors to make sure that the generalisation
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are clear where the metal performed very well to generalise
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and where it failed.
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Critical role needs to be defined
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on the model to make sure that fairness,
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trust and safety is there to safeguard
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the reinforcement.
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of inequalities and to ensure that compliance with
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ethical standards is achieved.
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Improvement strategies,
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always make sure that improvement strategies are there on the models.
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One can always use techniques like
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data augmentation,
18:41
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reweighting,
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fairness, awareness training,
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then to help to mitigate against the disparities.
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Ethical consideration continues,
18:51
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but now I'm looking at ethical considerations.
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And then these are the issues that I looked at when
18:59
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I looked at ethical consideration.
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I looked at data privacy to ensure that originally the
19:05
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concern and anonymization of sensitive data or personal information
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is adhered to algorithm fairness.
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This ensures that balance false positive and
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false negative are avoided so that the harmful issues
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are taken care of in the real
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world application of the models.
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Bias and harm,
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this needs to be addressed because it will mitigate
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against the risk for bias data and model decision.
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when you apply this to cater for inequalities.
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Ethical reflection always makes sure that when
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the model is applied there's always
19:54
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that ethical reflection to ensure that fairness in
19:58
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specific use cases
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and recommend models are always there to validate across
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subgroups for equal deployment.
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The next slide looks at the
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model deployment.
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We are not required to deploy the model per se,
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but the requirement was that
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we provide the consideration how the model will be deployed.
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This is the workflow that I've developed to deploy the model.
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The first step is to do data collection and processing.
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On the data collection and processing,
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one gets data from different sources.
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You do data cleaning using ETA strategies
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or methods,
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and then one also apply feature engineering on the data set.
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Once this is done,
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the clean data is then passed to the model training and validation.
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The model,
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the validation and training,
20:51
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this is where the model is trained on the data.
20:54
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
You do validation of the model where you use different matrices,
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explainability or performance.
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S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
And then you also check for fairness checks.
21:03
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Once this is done,
21:05
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then you pass the model to the deployment.
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This is where then the model is deployed.
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And it is recommended that you use it when the cloud platform
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is used, for example,
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like Azure.
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When deploying the model,
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one can also deploy it with APIs.
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Once the model is deployed,
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then monitoring and scaling is applied,
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and then this is where you track your matrices to auto
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-scaling to improve performance,
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and then also check bias and drifting alerts on the model.
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Once that is done,
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then based on the output or the results from the monitoring
21:41
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and scaling,
21:42
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One is to retrain the model.
21:44
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How do you then retrain the model?
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You go back and connect more data to feed into the
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model, then based on that you update the model and then you
21:55
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go back,
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retrain the model.
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That's how I propose the deployment of the model.
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The next slide looks at the piece of
22:06
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the notebook that I've developed,
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just to indicate that the model does perform,
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does work.
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This is just a demonstration on how the model works.
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Yes,
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this is how the model works.
22:42
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
So this is how I developed the model.
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And this piece of the model,
22:48
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actually what it does is it is
22:53
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from the notebook,
22:53
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the entire notebook.
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Then how does it perform?
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It does the data pre -preparation and then it does
23:00
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the visualization pipeline for the chest X -rays.
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What does it do?
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How does it do that?
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S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
It loads the images.
23:08
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pre -process the images,
23:10
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create labels,
23:12
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arrays,
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applies the data augmentation,
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and finally produces the visualization of the chest
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X -rays.
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That should show that the entire model does work and it does produce
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the visualization of the X -rays.
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The next slide looks at the choice of the tools.
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Why did I choose the tools that I chose for the model?
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The SCETEC Learn,
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it was applied on classical
24:27
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models.
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The reason why it was applied,
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it was not because it is an advanced tool,
24:33
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
but it was applied because it provides that transparency to
24:38
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
compare the deep learning pipeline against the...
24:41
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
the classical machine learning model.
24:44
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So it provides that comparison between the two.
24:48
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
Why TensorFlow on deep learning?
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S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
TensorFlow on deep learning was used to show how the DL models
24:55
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can learn features automatically and scale the
24:59
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large dataset with extensions and transfer learning.
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Why use SHARP unexplainability?
25:06
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SHARP was used on explainability for the following reasons.
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It makes black box.
25:12
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That means it allows the comparison of the
25:16
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SVM and the CNN results to be interpretable.
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S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
And then it helps with understanding why the prediction was
25:25
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made, not just to know what the prediction is.
25:28
S… Speaker 2 (Comparative Intelligence and Ethics in Machine Learning Systems)
So it gives you that understanding why was this prediction made.
25:31
S… Speaker 1 (Comparative Intelligence and Ethics in Machine Learning Systems)
And then it also provides mathematical consistency
25:35
S… Speaker 2 (Comparative Intelligence and Ethics in Machine Learning Systems)
features,
25:36
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attributes.
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based on the Shapley values.
25:38
S… Speaker 2 (Comparative Intelligence and Ethics in Machine Learning Systems)
And lastly,

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