I've been keeping an eye on the use of machine learning algorithms, particularly by venture capitalists, to make investment decisions for some time now. They've been investing in machine learning companies for years, so applying their products to other businesses, once you have studied how they work, seems a reasonable proposition. After all, what is the decision to invest in a startup based on? Basically, the fruit of a set of analyses and previous experiences that can be systematized and verified in different ways, while the experience corresponds, in reality, to the imperfect distillation, with its biases and errors, of a series of previous decisions, weighted by the results obtained in each. That said, venture capitalists are not entirely objective: they usually allow multiple factors to enter the decision-making process, which include anything from the feelings generated by the company's founding team, to more or less rigorous analyses of its capacity for future development.
Global research firm Gartner predicts that 75% of venture capitalists and private equity investors will use artificial intelligence (AI) to make their investment decisions by 2025. Investing in startups is just as, if not more, risky than investing in the money market. Many companies like Motherbrain and SignalFire are already using data to track down companies that are on the cusp of becoming successful. A person's'gut feel' is often the compass for making decisions. However, when it comes to investing in companies and startups, research and advisory firm Gartner estimates that three-fourths of the venture capitalists (VCs), globally, will be using artificial intelligence (AI) to make their decision by 2025.
The University of Hawaii reports that big data is shaking up the venture capital industry in unbelievable ways. Venture capitalists are finding new ways to leverage alternative data effectively for much higher yields. Big data plays a role in shifting the risk-reward calculus in the favor of venture capitalists. Venture capital is a high risk, high reward game. To put it into perspective, 90% of new startups fail, which means that investors can lose a lot of money while hunting the potential "unicorns."
Algorithmic or automated decision systems use data and statistical analyses to classify people for the purpose of assessing their eligibility for a benefit or penalty. Such systems have been traditionally used for credit decisions, and currently are widely used for employment screening, insurance eligibility, and marketing. They are also used in the public sector, including for the delivery of government services, and in criminal justice sentencing and probation decisions. Most of these automated decision systems rely on traditional statistical techniques like regression analysis. Recently, though, these systems have incorporated machine learning to improve their accuracy and fairness. These advanced statistical techniques seek to find patterns in data without requiring the analyst to specify in advance which factors to use. They will often find new, unexpected connections that might not be obvious to the analyst or follow from a common sense or theoretic understanding of the subject matter. As a result, they can help to discover new factors that improve the accuracy of eligibility predictions and the decisions based on them.
The growing use of artificial intelligence in sensitive areas, including for hiring, criminal justice, and healthcare, has stirred a debate about bias and fairness. Yet human decision making in these and other domains can also be flawed, shaped by individual and societal biases that are often unconscious. Will AI's decisions be less biased than human ones? Or will AI make these problems worse? Will AI's decisions be less biased than human ones?