On Cross Company AI

 Very interesting, another example.  Which leads us to the usual difficulty of adequately model different corporate goals.

Intelligence Across Company Borders

By Olga Fink, Torbjørn Netland, Stefan Feuerriegelc

Communications of the ACM, January 2022, Vol. 65 No. 1, Pages 34-36   10.1145/3470449

Artificial intelligence (AI) has potential to increase global economic activity in the industrial sector by $13 trillion by 2030.6 However, this potential remains largely untapped because of a lack of access to or a failure to effectively leverage data across companies borders.10 AI technologies benefit from large amounts of representative data—often more data than a single company possesses. It is especially challenging to achieve good AI performance in industrial settings with unexpected events or critical system states that are, by definition, rare. Industrial examples are early detections of outages in power systems or predicting machine faults and remaining useful life, for which robust inference is often precluded.

A solution is to implement cross-company AI technologies that have access to data from a large cross-company sample. This approach can effectively compile large-scale representative datasets that allow for robust inference. In principle, this could be achieved through data sharing. However, due to confidentiality and risk concerns, many companies remain reluctant to share data directly—despite the potential benefits.10 In some cases, data sharing is also precluded by privacy laws (for example, when involving data from individuals). Likewise, sharing code for AI models among companies has other drawbacks. In particular, it prevents that AI learns from large-scale, cross-company data, and, hence, potential performance gains from cross-company collaboration would be restrained.

To overcome the limitations of direct data sharing, we discuss a potential remedy by using federated learning with domain adaptation. This approach can enable inference across company borders without disclosing the proprietary data. Earlier works discuss the importance of AI in interorganizational settings (for example, via meta learning or transfer learning). For instance, in Hirt et al.,3 a prediction ensemble across different interorganizational entities is built, which is effective when all entities solve the same task. What makes federated learning combined with domain adaptation appealing is its flexibility when operating conditions vary across companies: it allows one to train a collaborative model that is tailored to the specific application and the specific conditions of a company.

Hurdles in Collaborating on Artificial Intelligence

Two prime hurdles hinder companies from collaborating in AI initiatives. First, a privacy-preserving solution is required so that inference can be made without disclosing the underlying data.10 Physical sharing of data could disclose proprietary information on operational processes or other intellectual property to competitors. This is particularly problematic whenever companies seek AI collaboration with suppliers, customers, or competitors. For example, data from manufacturing plants could reveal parameter settings, product compositions, throughput rates, yield, routing, and machine uptimes. If such data is revealed, it can be misused by customers in negotiations or help competitors improve their productivity or products. Besides intellectual property, a number of further constraints are reducing companies' propensity to share data. Examples include trust, cybersecurity risks, ethical constraints, and laws for ensuring a user's right to privacy.

The second hurdle is that collaborating companies need to account for the possibility of domain shifts. A domain shift refers to discrepancies among the data distributions collected for systems with different configurations or operating conditions.9 For example, machine data from one company may not be representative of operating conditions observed in another company. A domain shift presents hurdles to the underlying inferences: a model that was trained on data from one company is likely to perform poorly when deployed at another company with distinctly different settings or conditions.

Toward Artificial Intelligence Across Companies

Recent advances in AI research can help overcome these two hurdles. Specifically, we review how cross-company AI can be achieved through a combination of federated learning to address the privacy-preserving data-sharing hurdle and domain adaptation to address the domain shift hurdle (see the accompanying figure). Such a combination is typically referred to as federated transfer learning.4,a  .....