![]() Meanwhile, sales from voice communications services fell slightly due to delayed funding for some of Black Box's government clients and sales of technology products rose 5% on healthy demand in North America and Europe. ![]() Revenue rose 7% for the company's data infrastructure services unit on strong demand from the business services, financial services, retail, and manufacturing markets in North America. Historicallly a profitable enterprise, Black Box recorded a fiscal loss in 2012 due in part to an uptick in operating costs, particularly a goodwill impairment loss for the year. Since that time, the company's revenues have hovered in that vicinity its sales in 2012 were nearly $1.1 billion. Its product partners include such providers as Cisco Systems, NEC, Polycom, and ShoreTel.īlack Box crested the $1 billion mark with its sales in 2007. Key industries served include business services, manufacturing, banking, retail, and health care.Ībout 60% of the company's sales are made to large organizations with sales over $1 billion, which includes companies and federal government entities, but Black Box also markets to small and mid-sized businesses. Black Box sells to corporations, schools, and government agencies primarily in North America and Europe. Most of the company's sales come from its on-site services such as design, installation, technical support, and maintenance. Black Box primarily distributes and services third-party equipment, some of which carries its brand, but it also manufactures some products. Products include modems, routers, switches, and testing equipment, as well as cabinets, cables, and training materials. The company distributes and supports voice and data networking infrastructure. Engage with researchers, think tanks, and innovators to adopt leading practices and cutting-edge tools to address algorithmic risks.There's no mystery behind how Black Box does business.Conduct periodic independent auditing or validation of algorithms based on established baseline parameters to test the validity of the training data, assess security against manipulation, and optimize model performance.Develop and maintain surveillance processes to help monitor outcomes from algorithms.Institute standardized disclosure practices to inform relevant stakeholders when decisions affecting them are being made using algorithms.Review black box algorithms (internal and external) and establish controls across the algorithm life cycle, including data gathering, preparation, model selection, training, evaluation, validation, and deployment.Assess the AI applications inventory and develop AI risk management strategy and governance structure, covering areas such as policies, training, roles, and responsibilities.Organizations should consider seeking transparency and accountability in how decisions are made by algorithms consider ethics, fairness, and safety in how algorithms are used and adopt new approaches to effectively manage the novel risks introduced by complex AI algorithms. This exposes the organization to vulnerabilities, such as biased data, unsuitable modeling techniques, and incorrect decision making, in the algorithm lifecycle.Īs algorithms become more powerful, pervasive, and sophisticated, the methods for monitoring and troubleshooting them lag behind adoption. If they produce results without explanation, they make detection of inappropriate decisions difficult. These applications often operate like “black boxes” for decision making. Organizations have begun using artificial intelligence (AI) techniques and solutions to affect outcomes across a broad range of purposes, such as approving loans, identifying fraudulent transactions, and performing surveillance. This article is one of nine trends outlined in Deloitte's Future of risk in the digital era report.
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