October 22, 2019 Public Contract Law Journal

Automated Source Selection Scoring & Far Compliance

by Brian Haney

Brian S. Haney is the Chief Executive Officer of Martian Technologies, Co. Special thanks to Professor Veronica Root, Shannon Lewry, Esq., Daniel Murray, Professor Ron Dolin, Dylan
Tilbury, Esq., Branden Keck, Ronald Hirsch, and Angela Haney for all of the helpful criticisms and valuable feedback.

I.  Introduction

On Earth today there are over five billion mobile phone users, almost four and a half billion internet users, and over three billion email users.1 Technology is a better way of doing things, providing an order of magnitude improvement in the cost-efficiency of virtually every professional industry. Indeed, one of the principal miracles of technology is “it allows us to do more with less.”2 Technological development is governed by Moore’s Law, which predicts that every two years the processing power of computers doubles, while its costs halve.3 The past fifty years have proven Moore’s Law correct.4 As a result, your cell phone has more computing power than all of NASA had in 1969, when Apollo 11 landed on the moon.5 It is “inconceivable that technology will radically alter all corners of our economy” and yet regulatory compliance “will be exempt from any change.”6 One area of the law that may benefit from technological advancement is public procurement law. Indeed, regulatory compliance is a rapidly growing field in government contracts practice and scholarship.7 Due to headlines detailing monumental fines imposed on corporations for compliance failures,8 most legal scholarship focuses on corporate compliance. However, government agencies are also tasked with significant regulatory compliance obligations. This is especially true in the context of public procurement and acquisition.9

The main source of law regulating government procurement officials is the Federal Acquisition Regulation (FAR).10 According to the United States Air Force, “[a]cquisition regulations and policies form an intricate maze that is impossible to navigate, raise[s] costs, and prolong[s] the time to deliver capabilities.”11 Further, “[m]any federal workers and contractors report it is daunting to decipher the thousands of pages of intricate federal and defense acquisition regulations, let alone become familiar with them.”12 Nevertheless, the federal government continues to spend nearly $4 trillion a year on procurement, of which roughly $700 billion is defense related.13 Thus, the Air Force has taken up the task of developing technology to “help acquisition professionals make sense of complex acquisition regulations” and accelerate the process of buying goods and services.14 Indeed, technology is the only solution capable of scaling up to solve the problems that lawmakers, regulators, and agency officials face.15

Technological-based solutions to the problem of convoluted and confusing regulations are a recent trend.16 Indeed, new regulatory technologies consist of computer algorithms performing a range of functions, from predictive analytics to natural language processing.17 These algorithms create new knowledge using inference, statistical, and logical reasoning techniques.18 The use of such technologies in the context of public procurement has the potential to drastically cut costs for governments, law firms, and businesses alike.19

This article argues for the use of a standardized algorithm in the source selection process to aid federal agencies in source selection compliance.20 Part II will provide a general overview of government agencies’ goals for public procurement and the source selection process. Part III will identify three common types of source selection compliance failures that occur during the federal acquisition process including: conversion of a best value procurement to a lowest-price technically acceptable (LPTA) procurement, mathematical scoring errors, and public corruption. Part IV will introduce an algorithm government agencies may use to standardize and automate portions of the source selection process. Part V will explain how the algorithm presented in Part IV aids in the prevention of agency compliance failures discussed in Part III. Part VI will identify and address potential objections to this article’s thesis.

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