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dan fiehn

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This week, AI Fairness. In blissful bias we trust?


Welcome to The Digital Eye, your weekly roundup of the latest technology news. 

Our team of experts have scoured the internet for the most exciting and informative articles so that you can stay up-to-date on all things digital, data, blockchain, AI & analytics.

How to Remove Bias in Machine Learning Training Data

Understanding & Reducing Bias In Training Data For AI/ML Projects

Much has changed in the AI/ML world but the concept of ‘garbage in; garbage out’ remains stoic. Any algorithm is only as good as its training data. And, no training data is without bias, not even the ones generated through automation.

In the past, many machine learning algorithms have been unfair to certain religions, races, genders, ethnicities, and economical statuses, among others. The Watson supercomputer from IBM that gave suggestions to doctors using a dataset of medical research papers was found to favor reputable studies only. Amazon’s recruiting algorithm was found to favor men over women.

COMPAS, a risk assessment algorithm used by many state judges in the USA to assist sentence determination, was disputed for assigning a higher reoffense risk factor in violent crimes to Black people. In 2016, Microsoft’s one-day experiment with a Twitter chatbot (that learned from other tweets) ended badly when the bot released 95000 tweets in 16 hours, most filled with misogyny, racism, and anti-semitic ideas.

What should developers know about machine learning?

As artificial intelligence and machine learning become increasingly important in the world of enterprise development, programmers need to know the ins and outs of AI and ML.

Five ML links to share

Here are five key resources every developer should read to stay on top of the machine learning trend:


  1. What is machine learning?
  2. How do you build a machine learning model?
  3. How does data science and machine learning differ?
  4. What is happening with enterprise machine learning?
  5. What does a machine learning engineer do?

The Technological Arms Race of Software Licensing

Open Source Technology or Technologies as Abstract

Software licensing exists in the space between digital services and the real world.

Often, a license will denote the number of active users permitted to use the software, as well as their ability to manipulate the source code given. As such, it is a contract often enforced by the software itself, with checks to ensure that certain parameters are met and that the license agreement has not been broken. For example, logging in and using a service may log other users out, or prevent login entirely if logged in elsewhere. It is not all-encompassing, though.

Services like Spotify allow the user to listen to music on their personal device but do not grant them the right to use those songs in a commercial setting. A case like this would be difficult to enforce on the software level since the device wouldn’t know if it was playing for an individual or playing the music for a store. 

    Artificial ‘inventors’ are pushing patent law to its limits

    AI presents a watershed challenge that requires us to think once again carefully about how to reward and encourage innovation.

    It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties.

    Fortunately, the search proved successful. But it wasn’t a human who found the promising molecules. It was a machine learning program.

    One compound has been patented under the name Halicin in homage to HAL, the artificial intelligence (AI) in Arthur C Clarke’s classic 2001: A Space Odyssey. Halicin works differently from existing antibiotics, disrupting the bacteria’s ability to access energy, and researchers hope bacteria may struggle to develop resistance to it.


    Do businesses really need real-time analytics?

    Real-time database startups enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations. But do everyday enterprises really need lightning-fast real-time data?

    The term “real time” has been infused throughout tech, from real-time stock picks to real-time pizza tracking. But there’s near-real time, and then there’s real time.

    As everyday enterprises begin incorporating data tools and tactics used inside the biggest of big tech companies, a sector of data services providers has emerged to help them take advantage of the truly real-time analytics and machine learning approaches only giant companies with far larger database teams and resources could have afforded in the past.

    Companies like Hazelcast, Rockset, Tecton and others enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations that respond to what you just clicked.



    Featured Article


    AI Fairness. In Blissful Bias, We Trust?

    Explanation methods that help users determine whether to trust machine-learning model predictions can be less accurate for disadvantaged subgroups, a new study finds.

    AI Fairness. In Bias We Trust?

    Article by @MIT

    When the stakes are high, machine-learning models are sometimes used to aid human decision-makers. For instance, a model could predict which law school applicants are most likely to pass the bar exam to help an admissions officer determine which students should be accepted.

    These models often have millions of parameters, so how they make predictions is nearly impossible for researchers to fully understand, let alone an admissions officer with no machine-learning experience. Researchers sometimes employ explanation methods that mimic a larger model by creating simple approximations of its predictions. These approximations, which are far easier to understand, help users determine whether to trust the model’s predictions.




    How to Maximise Knowledge Transfer Between Business and Engineering Teams

    In this week’s feature article, discover how to maximise knowledge transfer between business experts and technical engineers to ensure the success of your digital transformation. Learn about creating structured knowledge transfer plans, fostering a learning environment, employing diverse transfer methods, and building trust and collaboration. These strategies will help you bridge knowledge gaps, boost innovation, and enhance operational efficiency. Dive into our expert insights to master the art of seamless collaboration and drive your organisation’s digital transformation forward.

    Strategic Planning Approaches: Top-Down vs. Bottom-Up

    Unravel the complexities of strategic planning with our latest exploration into Strategic Planning Approaches. Whether you’re leaning towards a top-down, bottom-up, or sideways strategy, our insight will guide you through the pros and cons of each method, helping you make informed decisions that align with your business objectives. Perfect for strategists and business leaders aiming to optimize their planning process and drive effective outcomes.

    Digital Sustainability Practices: A Guide to Greener Habits

    Explore the transformative power of digital sustainability practices. This guide unveils how adopting eco-friendly digital habits can significantly reduce your carbon footprint and pave the way for a greener future. Dive into practical tips and strategies for making a positive environmental impact through digital activities.