I would say, you're looking for not only excellent reviews from true peers, so if you see a lot of reviews from big law firms and you're operating a small law firm, that with a grain of salt and do your due diligence to make sure that it applies to your firm as well and see what the reviews are like from true peers of yours, and then also internally, I would also say that the ability to play well with other's compatibility, the ability to communicate with other software that you're using, does it have an open API, for example, Clio practice them the rocket matter like a lot of these companies are playing very well with others and you can even see on their websites their integration partners. Some of them have full-on marketplaces of their integration partners that are available as a buffet of all the different software partners that you can plug in to these systems, and that's what you're looking for because it affords you flexibility and not being chained to a certain string of software or services that only play with each other which means that you are tethered to using this kind of preformed kit that may not best serve your needs.

Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past decisions and reproduce unfair decisions. In this paper, we propose two algorithms that adjust fitted ML predictors to make them fair. We focus on two legal notions of fairness: (a) providing equal opportunity (EO) to individuals regardless of sensitive attributes and (b) repairing historical disadvantages through affirmative action (AA). More technically, we produce fair EO and AA predictors by positing a causal model and considering counterfactual decisions. We prove that the resulting predictors are theoretically optimal in predictive performance while satisfying fairness. We evaluate the algorithms, and the trade-offs between accuracy and fairness, on datasets about admissions, income, credit and recidivism.

In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems \emph{fair regression}. We propose general schemes for fair regression under two notions of fairness: (1) statistical parity, which asks that the prediction be statistically independent of the protected attribute, and (2) bounded group loss, which asks that the prediction error restricted to any protected group remain below some pre-determined level. While we only study these two notions of fairness, our schemes are applicable to arbitrary Lipschitz-continuous losses, and so they encompass least-squares regression, logistic regression, quantile regression, and many other tasks. Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions. In addition to analyzing theoretical properties of our schemes, we empirically demonstrate their ability to uncover fairness--accuracy frontiers on several standard datasets.

The causes underlying unfair decision making are complex, being internalised in different ways by decision makers, other actors dealing with data and models, and ultimately by the individuals being affected by these decisions. One frequent manifestation of all these latent causes arises in the form of missing values: protected groups are more reluctant to give information that could be used against them, delicate information for some groups can be erased by human operators, or data acquisition may simply be less complete and systematic for minority groups. As a result, missing values and bias in data are two phenomena that are tightly coupled. However, most recent techniques, libraries and experimental results dealing with fairness in machine learning have simply ignored missing data. In this paper, we claim that fairness research should not miss the opportunity to deal properly with missing data. To support this claim, (1) we analyse the sources of missing data and bias, and we map the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should not be treated as the uncomfortable ugly data that different techniques and libraries get rid of at the first occasion, and (3) we study the trade-off between performance and fairness when the rows with missing values are used (either because the technique deals with them directly or by imputation methods). We end the paper with a series of recommended procedures about what to do with missing data when aiming for fair decision making.

CEO at Things Foundation, Technology Manager at Let's Do It Foundation Head of Cortex Ventures- Africa's first AI only focused VC Fund

Decision makers are becoming more wary about certain uses of AI, including trusting solutions to make money for them. When things go wrong and losses are up, it's tough to know who's responsible – the machine, or the person behind the machine. Bloomberg reports on a recent example of this, where an investor is suing for damages after losing money based on the decisions made by a money management solution. According to Bloomberg, back in 2017, investor Samathur Li Kin-kan bought into a money management AI solution that another investor, Raffaele Costa, planned on using to mange the money made by his company, Tyndaris. "The idea of a fully automated money manager inspired Li instantly," driving him to invest in the solution to grow his own money – $2.5 billion- $250 million worth.

Decision makers are becoming more wary about certain uses of AI, including trusting solutions to make money for them. When things go wrong and losses are up, it's tough to know who's responsible – the machine, or the person behind the machine. Bloomberg reports on a recent example of this, where an investor is suing for damages after losing money based on the decisions made by a money management solution. According to Bloomberg, back in 2017, investor Samathur Li Kin-kan bought into a money management AI solution that another investor, Raffaele Costa, planned on using to mange the money made by his company, Tyndaris. "The idea of a fully automated money manager inspired Li instantly," driving him to invest in the solution to grow his own money – $2.5 billion- $250 million worth.

Alexa's poor reputation for privacy may soon worsen as a patent filed by the firm suggests the virtual assistant may start listening before its'wake word' is said. Under the plans Alexa will be able to detect when it is being given a command even if the wake word is said at the end of the sentence instead of at the front. The move raises concerns over user privacy as Alexa will, by default, always be listening to conversations on the off-chance its wakeword is spoken. Alexa's poor reputation for privacy may soon worsen as a patent filed by the firm suggests the virtual assistant may start listening before its'wake word' is said. The patent, filed with the US Patent and Trademark Office, reveals the Seattle-fimrs plans for the next evolutionary step for it Alexa's technology.

Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, there is increasing social and legal pressure to provide explanations that help the affected individuals not only to understand why a prediction was output, but also how to act to obtain a desired outcome. To this end, several works have proposed methods to generate counterfactual explanations. However, they are often restricted to a particular subset of models (e.g., decision trees or linear models), and cannot directly handle the mixed (numerical and nominal) nature of the features describing each individual. In this paper, we propose a model-agnostic algorithm to generate counterfactual explanations that builds on the standard theory and tools from formal verification. Specifically, our algorithm solves a sequence of satisfiability problems, where a wide variety of predictive models and distances in mixed feature spaces, as well as natural notions of plausibility and diversity, are represented as logic formulas. Our experiments on real-world data demonstrate that our approach can flexibly handle widely deployed predictive models, while providing meaningfully closer counterfactuals than existing approaches.

"Overlearning" means that a model trained for a seemingly simple objective implicitly learns to recognize attributes that are (1) statistically uncorrelated with the objective, and (2) sensitive from a privacy or bias perspective. For example, a binary gender classifier of facial images also learns to recognize races--even races that are not represented in the training data--and identities. We demonstrate overlearning in several image-analysis and NLP models and analyze its harmful consequences. First, inference-time internal representations of an overlearned model reveal sensitive attributes of the input, breaking privacy protections such as model partitioning. Second, an overlearned model can be "repurposed" for a different, uncorrelated task. Overlearning may be inherent to some tasks. We show that techniques for censoring unwanted properties from representations either fail, or degrade the model's performance on both the original and unintended tasks. This is a challenge for regulations that aim to prevent models from learning or using certain attributes.