Context

Businesses are increasingly utilising algorithms to improve their pricing models, enhance customer experience and optimise business processes. Governments are employing algorithms to detect crime and determine fines. Consumers are benefitting from personalised services and lower prices. However, algorithms have also raised concerns such as collusions and malfunctioning, privacy, competition issues, and information asymmetry.

Algorithm Collusion

• Automated systems have now made it easier for firms to achieve collusive outcomes without formal agreement or human interaction, thereby signalling anti-competitive behaviour.

• This results in “tacit algorithmic collusion”, an outcome which is still not covered by existing competition law.

Case study –  This can occur in non-oligopolistic markets too. In 2015, US Federal Trade Commission fined David Topkins (former e-commerce executive of a company selling online posters and frames), for fixing the price of certain posters sold through Amazon Marketplace using complex algorithms, impacting consumer welfare and competition adversely. 

Security Concerns from collusion alogotithm

1. Negligence of private data –

• In order to enjoy services at low or zero price, consumers neglect the value of their data.

• Access to easily procurable data such as Facebook “likes” can be used to target only advantageous customers circumventing anti-discrimination mechanisms.

2. Ransomware attack –

• Application of advanced algorithms have also resulted in an increase in ransomware attacks.

• A devastating cyber attack — the WannaCry ransomware attack — hit the world in May 2017, affecting around 2,30,000 computers across 150 countries.

3. Competition –

• Important concerns pertain to “competition” as well.

• Processing of large datasets through dynamic algorithms generate real-time data “feedback loops”, impacting competition adversely.

• As more users visit select platforms, not only more data, but data with greater reliability is collected, allowing firms to more effectively target customers. Consequently, more users feedback into this loop. 

Case Study – That Google has been estimated to charge a higher cost-per-click (CPC) than Bing, a competitor, suggests that advertisers attribute a higher probability of converting a viewer of Google’s ads into a customer.

4. Complexity of system –

• Then, we have evolving machine-learning algorithms ranging from voice recognition systems to self-driving cars.

• Even high-profile programmers/developers may not be able to trace the working of such algorithms making nearly impossible the identification of any anti-competitive practice.

Conclusion

A rethink of public policy is absolutely essential if non-desirable impacts of artificial intelligence on human race are to be arrested.