Cogito Artificial Intelligent (AI) Software - An Ethics Case Study

AI ethics in voice interactions

Cogito software is an Artificial Intelligence (AI) system that provides call center agents with real-time feedback and conversational guidance to enhance customer experience. Backed by behavioural science, Cogito is unique because it gives human call center agents live suggestions from concepts such as their empathy levels and pace. It is a human-AI interaction software that has proven successful for call centers in the healthcare and finance industries. The Markkula Framework is used in analyzing the Cogito Software from an ethical perspective by applying Consequentialism, Deontology and Virtue Ethics theories. In this paper, I focus on the ethical perspectives of the Cogito software.

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“Human in the loop” is a popular way to mitigate the risks of AI. That approach might be doomed

Human and robot

I’ve written in the past about how fuzzy the line between “good” and “evil” data science and artificial intelligence (AI) can be. Ethical issues arise with AI that are neither clear cut nor easy to navigate. One of the popular ways to mitigate the risks of AI is to pursue a “human in the loop” strategy where people still retain ultimate decision authority for major decisions. In this blog, I’ll explain why that approach may be doomed for failure as a primary tool for stopping “evil” AI from being deployed.

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Six questions about Data Science Ethics

Aristotle

During the years of Facebook’s ascent, Mark Zuckerberg’s motto was famously, “Move fast and break things.” Well, since those days, a lot has been broken. Despite the various benefits big tech affords our lives, companies’ sole focus on fast growth has meant that ethical considerations of how their products might harm society have only been afterthoughts, something to apologize for later after the damage has been done.

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Coding ethics for AI & AIOps: Designing responsible AI systems

Designing responsible systems

Can you relate to this image ? This is a typical log file that support / dev teams have struggled – manually reading the logs line by line to resolve an outage/anomaly. Such was the era of traditional IT operations where : Process was time consuming, correlation between different layers of platform and multiple log files was difficult; Results could vary & valid for a particular time duration; Results could be lost and history wasn’t saved and Thus this approach did not scale.

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Foundations of Ethical Artificial: Concepts and principles of explainability and trust

Ethics - explainability & trust

Widespread use of Artificial intelligence (AI) technologies in financial institutions and financial crime risk management systems have encouraged debate around the ethical challenges and risks AI-based technology pose. AI technologies have a significant impact on the development of humanity, and so they have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks are potentially involved, and how we can control these.

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Mindful Machines: Neuroscience & Critical Theory for Ethical AI

Mindful

In this series’ first article, regarding critical theory, I argue that intersectionality is important for AI Ethics, data is not objective, and that the structure of language has relevance. Regarding neuroscience, I discussed unconscious bias and introduced the topics of computational neuroscience, synaptic plasticity and symbolic AI, all of which will be explored further here. The purpose of this second part is to theorize a way forward in two steps.

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AI datasets are prone to mismanagement, study finds

AI datasets

Public datasets like Duke University’s DukeMTMC are often used to train, test, and fine-tune machine learning algorithms that make their way into production, sometimes with controversial results. It’s an open secret that biases in these datasets could negatively impact the predictions made by an algorithm, for example causing a facial recognition system to misidentify a person. But a recent study coauthored by researchers at Princeton reveals that computer vision datasets, particularly those containing images of people, present a range of ethical problems.

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Is the democratization of AI good?

Unisex sign and individual

In the modern age of education, almost anyone with an internet connection can learn anything they want to. This is also true for learning AI, and now, anyone with the requisite background has the opportunity to learn AI and build AI programs. When I say “democratization,” I mean the easy access to AI education and learning, and more importantly, the easy access to building scalable AI applications. In an article I wrote earlier this summer, I discussed my personal experience with AI ethics and how I paid little regard to the implications of my work.

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Ethics sheet for Automatic Emotion Recognition and Sentiment Analysis

Emotions play a central role in our lives. Automatic Emotion Recognition (AER) — or “giving emotional abilities to computers” as Dr. Rosalind Picard described it in her seminal book Affective Computing) — is a sweeping interdisciplinary area of study exploring many foundational research questions and many commercial applications. However, some of the recent commercial and governmental uses of emotion recognition have garnered considerable criticism.

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An AI’s guide to Utilitarianism

AI to the benefit of humankind

In his seminal paper, Human-Compatible Artificial Intelligence, Stuart Russell talks about “superintelligent AI” as an existential risk to humankind. One of the reasons, explains Russell, is the difficulty in defining a set of objectives for a machine, more intelligent than ourselves, that results in “beneficial outcomes for humans”. The challenge is, how do we know what something more intelligent than ourselves is going to do? How is it going to act upon the purpose that we give it?

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How to make your data project ethical by design

Man holding a light bulb

Data is the lifeblood of companies today. Not only does day-to-day functioning rely on a constant feed of data about every aspect of operations, it’s becoming increasingly clear that with enough data and the right analysis, previously intractable problems can be solved and processes improved. It should come as no surprise that data science is currently ranked #2 on Glassdoor’s 2021 list of best jobs in the US (and has been #1 for 4 of the past 6 years).

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Reflecting on company leadership and their understanding of AI

C-Suite board room

65% of executives cannot explain how their AI models make decisions. This isn’t a number that we should just brush aside. It’s a number that should, at a minimum, be disappointing. It doesn’t make sense that a CEO wouldn’t be able to explain how one of their tools works. Certainly the rapid adoption of AI tools, can make it difficult to keep up, but excuses shouldn’t be made for technology that can have life altering consequences.

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AI Ethics sells… But who’s Buying?

European Commision

The new EU draft AI regulation has leaked, reopening again the regulation vs. innovation debate. Is regulation really going to kill innovation in Europe? It has been already two years since the European High-Level Expert (HLE) group on Artificial Intelligence presented its Ethics Guidelines for Trustworthy Artificial Intelligence. The guidelines identified seven key requirements that AI systems should meet to be trustworthy.

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Do companies need a Chief AI-Ethics Officer?

Ethical workflow

The world we live in is becoming more and more data-driven. This is causing companies to make more and more use of AI techniques such as machine learning and deep learning. The task of the Chief AI Ethics Officer (CAIEO) should not be primarily technical. Instead, it should sensitize data scientists, machine learning engineers, and developers to ethical issues. The whole process should be firmly integrated into the respective process models and phases.

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Introduction to the 4 principles of the Responsible AI for Business Leaders

Robot torso

Artificial Intelligence (AI) technology facilitates decisions that have far-reaching consequences for everyone, and that’s why we need a solid and shared standard that ensures AI is safe, trustworthy, and unbiased, as well as that AI and Machine Learning (ML) models are robust, explainable, ethical, and efficient.

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