Data for All

Two months ago, I planned on launching the first edition of this newsletter. I had mapped out topics, identified case studies, and developed activities. I had every intention of launching in the first week of June. And then I didn’t.

I found myself overwhelmed by the prospect of trying to navigate the world of data and AI, given the incredibly rapid pace of the technology and the steady breaking news across the industry – and I’ve been working in data & technology for my entire career.

Over the last couple of months, I took my time to consider what value this newsletter could provide, and whether this venture was worth the time (and importantly if it is worth your time as well…). It doesn’t require you to have prior data or AI knowledge, let’s learn and discuss together.

This newsletter isn’t about covering the latest and breaking AI or data news; there are incredible resources out there – which I will reference and share whenever I come across one.

So without further ado – I’d like to welcome you to The Learning Algorithm.

I’m incredibly excited for what this newsletter could be, and for having you join me on this exploration of:

• Data & AI Enablement

• Understanding Data & AI from a human-centered perspective

• Data Ethics

• Data Egalitarianism

• Data & AI Literacy

• AI Adoption Frameworks

Before we begin, I do want to make the following pledges and declarations:

I am not a qualified data scientist, nor a lawyer (although I do enjoy watching Suits, so that’s basically the same as a Juris Doctor, right?)

My perspectives are from a nearly 20-year career in learning and development, enabling and engaging teams and companies with data and technology. I have practical working knowledge of bias, algorithms, analytics, workflows, and associated technologies to enable data & AI. I hold several certifications and believe in continual development through reading, courses, podcasts, etc. (always happy to discuss my bookshelf).

I will not be promoting any specific technology or platforms (no #ads or sponsored content).

My philosophy is the platform is generally irrelevant when it comes to successful data or AI application and should never be seen as the sole solution to a data problem. Although I don’t have any planned yet, I may test or interact with different tools and share any honest feedback that may be relevant.

I will be looking for this to be interactive and to hear from and engage with anyone who would like to participate.

This could be in interviews, case studies, polls and surveys, or just chatting in the comments below. If you have any topics or questions about the topics above, please let me know – I’d love to hear from you.

Disclaimer: My views and opinions as part of this newsletter are my own and do not reflect and are not attributable to any of my current or former employers.

Now that I’ve spent half of this inaugural edition talking about the newsletter – let’s get started.

Egalitarian Data and AI aims to create fair, unbiased, and inclusive artificial intelligence systems by addressing societal inequalities and promoting equal access to data and technology.

This is the underlying philosophy that influenced not only the development of this newsletter but also my approach to data and AI in general.

While we may think that modern society is the first to grapple with dystopic Skynet1 oppressive data or AI systems, to spoil an upcoming edition into the history of data (I promise it will be more interesting than it sounds…), we’ll be exploring the relationship between data and control over the centuries.

What does separate modern society from our ancestors is that due to the interconnectedness of our technology-focused world, the effects ripple across at a global scale. It is this interconnectedness that exacerbates the bias, permits unfairness, and withdraws our ability to be fully informed and consent to how not only we access data – but how our data is accessed by others.

Egalitarian Data & AI requires consideration beyond the is it possible / can a task be done. Egalitarianism requires us to consider:

• Should the task be done?

• Does it satisfy larger ethical or societal goals?

These are a higher watermark, and I freely admit can be difficult to meet.

Example: AI-assisted hiring practices There are a myriad of examples over the last decade of AI tools being used in the recruitment and hiring processes, including most publicly Amazon.

A system can technically review a collection of resumes, identify patterns, make connections, and rank or ‘prioritize’ based on an internal or externally provided list of attributes. Indeed, since the GenAI boom over the last two years, these have only become more prevalent, with hundreds of AI-enhanced recruitment tools available.

However, can this system also develop unintended connections, prioritizing resumes based on criteria not provided but meeting historical trends, such as promoting male applicants over female applicants?

Is it simply a time-saving venture? Or in an attempt to remove the bias of human reviewers, have new biases been introduced?

Can an applicant, aware of the weaknesses or limitations of AI, tailor a resume or include particular language regardless of its relevance, to ‘jump the queue’? Is that a negative, or does it show keenness and creativity from the applicant?

Who is being disadvantaged by this system, and is this perpetuating already existing biases?

These are some of the topics that we’ll be exploring over the coming months, as we unpack not only the history of data and AI but its applications, the ethics that surround it, and how we can enable ourselves, our teams, and our organizations to engage with data safely and ethically.

I’d love to continue this conversation with you in the comments.

• What does Egalitarian Data & AI mean to you?

• What does it look like?

• Are there other topics you’d like explored?

Leave a comment

A couple of quick requests:

• If you’d like to share this with your colleagues or friends, I would be eternally grateful, as I’d like to make this conversation as diverse and engaging as possible.

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• A second request, I’m workshopping some closing lines, which one do you cringe less at / do you prefer:

Option A: Unlocking the power of data, one newsletter at a time.

Option B: Empowering your data journey, one byte at a time.

Thank you for joining me in this exploration of data & AI from what (I hope) is perhaps a slightly different perspective than you may usually hear from.

Yours in data,

Neil