janet yellen : the economic outlook testimony by ms janet l yellen, chair of the board of governors of the federal reserve system, before the joint economic committee, us congress, washington dc, 17 november 2016. * * * chairman coats, ranking member maloney, and members of the committee, i appreciate the opportunity to testify before you today. i will discuss the current economic outlook and monetary policy. the economic outlook the u. s. economy has made further progress this year toward the federal reserve ’ s dualmandate objectives of maximum employment and price stability. job gains averaged 180, 000 per month from january through october, a somewhat slower pace than last year but still well above estimates of the pace necessary to absorb new entrants to the labor force. the unemployment rate, which stood at 4. 9 percent in october, has held relatively steady since the beginning of the year. the stability of the unemployment rate, combined with above - trend job growth, suggests that the u. s. economy has had a bit more “ room to run ” than anticipated earlier. this favorable outcome has been reflected in the labor force participation rate, which has been about unchanged this year, on net, despite an underlying downward trend stemming from the aging of the u. s. population. while above - trend growth of the labor force and employment cannot continue indefinitely, there nonetheless appears to be scope for some further improvement in the labor market. the unemployment rate is still a little above the median of federal open market committee ( fomc ) participants ’ estimates of its longer - run level, and involuntary part - time employment remains elevated relative to historical norms. further employment gains may well help support labor force participation as well as wage gains ; indeed, there are some signs that the pace of wage growth has stepped up recently. while the improvements in the labor market over the past year have been widespread across racial and ethnic groups, it is troubling that unemployment rates for african americans and hispanics remain higher than for the nation overall, and that the annual income of the median african american household and the median hispanic household is still well below the median income of other u. s. households. meanwhile, u. s. economic growth appears to have picked up from its subdued pace earlier this year. after rising at an annual rate of just 1 percent in the first half of this year, inflation - adjusted gross domestic product is estimated to have increased nearly 3 percent in the third quarter. in part, the pickup reflected some

. federalreserve. gov / aboutthefed / files / fac - 20201203. pdf. rayid ghani, “ equitable algorithms : examining ways to reduce ai bias in financial services ” ( testimony before the house committee on financial services task force on artificial intelligence hearing on february 12, 2020 ), https : / / www. congress. gov / 116 / meeting / house / 110499 / witnesses / hhrg - 116 - ba00 - wstate - ghanir - 20200212u1. pdf. black box problems recognizing the potential and the pitfalls of ai, let us turn to one of the central challenges to using ai in financial services — the lack of model transparency. some of the more complex machine learning models, such as certain neural networks, operate at a level of complexity that offers limited or no insight into how the model works. this is often referred to as the “ black box problem, ” because we can observe the inputs the models take in, and examine the predictions or classifications the model makes based on those inputs, but the process for getting from inputs to outputs is obscured from view or very hard to understand. there are generally two reasons machine learning models tend toward opacity. the first is that an algorithm rather than a human being “ builds ” the model. developers write the initial algorithm and feed it with the relevant data, but do not specify how to solve the problem at hand. the algorithm uses the input data to estimate a potentially complex model specification, which in turn make predictions or classifications. as michael tyka puts it, “ [ t ] he problem is that the knowledge gets baked into the network, rather than into us. have we really understood anything? not really — the network has. ” 10 this is somewhat different from traditional econometric or other statistical models, which are designed and specified by humans. the second is that some machine learning models can take into account more complex nonlinear interactions than most traditional models in ways that human beings would likely not be able to identify on their own. 11 the ability to identify subtle and complex patterns is what makes machine learning such a powerful tool, but that complexity often makes the model davide castelvevchi, “ can we open the black box of ai? ” nature 538 ( october 5, 2016 ) : 20 – 23, https : / / www. nature. com / news / can - we - open - the - black - box