11/8/2022 0 Comments Cyberduck aiLack of the right insights is killing B2B startups. The top reason startups fail is that they don’t understand their market well enough. And it's not only large organisations that fail with data. Cyberduck ai upgrade#When TSB experienced its data migration and system upgrade disaster, thousands of businesses were unable to pay staff and receive money. If it doesn’t flow smoothly, you could end up with a really nasty spill. When migrating data or developing new platforms, data is your oil. We’re going to answer these questions:īut first, let’s take a step back to look at the context around using our data. I’m going to cover how we can use UX and concepts such as service design, data preparation and innovative technologies to achieve digital transformation. Applying UX thinking to your insights can improve your digital assets, increase customer satisfaction and makes your organisation more efficient. How can you keep it manageable? How can you generate the right insights for genuine, impactful, strategic digital transformation? Here’s a secret. Fortunately, I know there are a lot of people and organisations involved in holding the AI industry accountable when it comes to data ethics and bias many of whom I am working with on an upcoming Data Ethics Hackathon from 17-20 Sept, where we'll be exploring ethical solutions to AI in Criminal Justice, Policing & Contact-Tracing.The tide of data we’re seeing shows no signs of stopping. Because when authorities, governments, police, healthcare, legal & justice systems rely on those algorithms to make decisions, those decisions will be inherently flawed…further perpetuating the imbalance. Interestingly, IBM announced last month (June 2020) they would no longer make general-purpose facial recognition software, citing concerns about how the technology is/can be used incorrectly by law enforcement agencies.Įradicating racial bias and creating equity in our society is a complex, long and in many ways, a dividing dilemma yet to be solved. Therefore there must be a responsibility for those involved in AI to not only address the implicit bias that exists within historical data sets used to train machines but to also invest time, resource and money into building diverse teams and ongoing programs of education within those teams to ensure the bias’ of their engineers do not make its way into the AI systems they are building. So when that technology is put to use, the outputs it generates will only further accentuate the bias, racism, and injustice found in society. If we get too carried away with creating AI algorithms without tackling inherent bias from the onset then we will end up with technology that “thinks” as flawed as us humans. There is often smoke-and-mirrors around the data of these AI tools too, making it difficult to properly interrogate its decisions. So when you consider law enforcement and the justice system has a history of disproportionately targeting certain groups - such as black people - the AI algorithm risks further reinforcing those issues. These AI risk assessment tools are trained using historical crime data. The theory is this should reduce judges' bias as decisions are data-driven.īut herein lies the problem. This score is taken into account by the judge when deciding whether someone is held in jail before trial, or how long their sentence should be. Some Police departments are using risk/harm assessment tools that are designed to consider a defendant’s profile and generate a score that estimates how likely they are to re-offend. When it comes to race, the problem we have is that systemic racism is so entrenched in today's society that it has resulted in significant racial bias and injustice. It is representative of a deeper issue surrounding ethics in data as many AI systems are trained using biased data, which will, of course, create biased outcomes. Image: Twitter / subtle discrimination may appear trivial, but it isn't.
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