Showing posts from June, 2019

Automated process and Algorithmic Audit. Understanding where your ML/ AI software came from?

Have been doing some work and thinking about automated decision making; specifically looking at a credit algorithm. Below are the questions I set the owner of the algorithm to help in the assessment. I am publishing them here in the hope they are helpful. 1. Where did the algorithm come from? What is it providence ? does it have a serial or product number? Which company supplied it ? (inc internal) Who maintains it ? How long has it been in the company? Is there any IP owned by other or the company? 2. Who wrote the algorithm ? ( company, team, individuals) What it a generic algorithm/ bespoke build? What was the acceptance procedure? What else did they write do for the company or others? 3. What data/ processes/ procedures were used to build it  Is it stand alone? What process it is part of ? Are you using the same processes now?  Did it replace a procedure? Where did the data come from to set the boundaries/ maths ?  4. What

Algorithms vs Processes. Subtle but important differences about bias and people in the loop

Algorithms vs Processes.  In both an input is transformed to an output – there is a difference in HOW.  Algorithm = a set of mathematical instructions or rules that, especially when coded enable a computer to calculate an answer. Typically, specific instructions that can be followed or learnt, that can be mechanised and reduces human involvement to zero. It is a tool it is driven with an idea about better or the most efficient resource allocation. An algorithm is a WHAT and HOW Process = a series or method of tasks/ steps/ actions that are taken in order to achieve a result. Typically, instructions that can be followed or learnt, that enable human and compute/ machine involvement to achieve a set goal, objective or result. A procedure is the prescribed way of undertaking a process. A process is a therefore a WHAT and a procedure is HOW Why important, we debate a lot about the dependence on algorithms in the computer age and how these algorithms will talk over the world and put

The Theory of Personal Data Mobility has become real hard evidence

The thinking that data mobility will create new economic value is 10 to 15 years old. I explored the growth potential in my Book “ My Digital Footprint ” in 2008 and I was building on existing economic ideas.  Fast forward 10 years and lots more thinking about the potential upsides and why sharing data creates value. An excellent 2018 report for the Department for Digital, Culture, Media and Sport (DCMS) Data Mobility: The data portability growth opportunity for the UK economy is such as report. To save some time; my summary of this report is here . Ctrl-Shift who wrote the report have gone on and created a Sandbox to showcase examples and bring the theory to life. The Sandbox is a cross-sector collaboration with Barclays, the BBC, BT, Centrica, Facebook and Independent observers ( check that outcomes are not made up of biased on one solution) to the Sandbox include the Centre for Data Ethics and Innovation (CDEI), Consumers International, the DCMS, t

Exploring why consent is really hard?

peeling back the layers — thanks to  James Abell  for the minecraft illustration. We love the model or analogy about peeling an onion. We peel back one layer to reveal a new similar layer, each layer enabling us to offer a new idea or thinking and adding complexity. Often we use this model for ourselves to get to our inner core and what values drive us. C onsent:  in digital context is being explored in many places by many people.  Kantara  and MEF are two good examples. However, I am finding that as I peel, explore and uncover the “onion” of layered consent, I find that the next layer is not more onion [ with deeper inner meaning driving me to a core philosophy ] but rather I find something totally new, indeed I don;t start with an onion but a coconut. Inside my coconut I find an orange, then a Kiwi, then a grapefruit, passing a passion fruit and then a dragon fruit. Peeling this inner core, I hope to find inner meaning but it only reveals a two spouted teapot! Why use di

what are "Safe People and Safe Projects" for data sharing

The start of this was a directed ideation from  Ian Oppermann  NSW Chief Data Scientist and CEO of NSW Data Analytics Centre. The Challenge: How do you design a privacy preserving data sharing framework based on these papers. They are well written and provide a very good framework for the question. Privacy in Data Sharing — A Guide for Business and Government (Nov 2018) Data Sharing Frameworks — Technical White paper (Sept 2017) A write up from the original work is here. Focus Safe People, Safe Projects Within this context, what is a safe project and safe people for a privacy perserviing data sharing idea? Where “safe” means in this context — privacy preserving. Ignoring other factors such as sensitivity, importance, ethics and outcomes. Assumption 1. The reconstruction/ re-identification problem PII (personally identifiable information) can be created from

Trust is not a destination

Trust is not a destination! The purpose of this thought piece is to bring together strategic thinking on data, governance and trust values into one argument. The recommendation is that boards need to wake up There are two existing models of trust that are relevant to business. Let’s call them “experience trust” and “emotional trust.” We are going to explore two new models of trust, explain why they are so disruptive and create a strawman as a way of thinking about the way forward. Experience trust  is simple to grasp. Think about using your bank card, pushing the brake pedal in a car, getting on a plane, charging your phone, posting a picture on Facebook, using a vape pipe, drinking water, taking a taxi, texting, etc. Every time you do something the ‘experience’ functions, within reason, as you expect it to. Expected feedback loops reinforce a message that whatever you use can be trusted. Society depends on experience trust. It makes life simple and convenient. As the old